In [ ]:
# This is the Exploratory data analysis of the various causes of deaths in the world from 1990 t0 2019.
In [1]:
#Importing relevant libraries
import pandas as pd 
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import re
%matplotlib inline
In [12]:
#To show all hidden columns
pd.set_option('display.max_columns', None)
In [87]:
#Loading the dataset
df = pd.read_csv('Global_causes_of_deaths.csv')
In [88]:
#Showing the first two columns
df.head(2)
Out[88]:
Entity Code Year Number of executions (Amnesty International) Deaths - Meningitis - Sex: Both - Age: All Ages (Number) Deaths - Neoplasms - Sex: Both - Age: All Ages (Number) Deaths - Fire, heat, and hot substances - Sex: Both - Age: All Ages (Number) Deaths - Malaria - Sex: Both - Age: All Ages (Number) Deaths - Drowning - Sex: Both - Age: All Ages (Number) Deaths - Interpersonal violence - Sex: Both - Age: All Ages (Number) Deaths - HIV/AIDS - Sex: Both - Age: All Ages (Number) Deaths - Drug use disorders - Sex: Both - Age: All Ages (Number) Deaths - Tuberculosis - Sex: Both - Age: All Ages (Number) Deaths - Road injuries - Sex: Both - Age: All Ages (Number) Deaths - Maternal disorders - Sex: Both - Age: All Ages (Number) Deaths - Lower respiratory infections - Sex: Both - Age: All Ages (Number) Deaths - Neonatal disorders - Sex: Both - Age: All Ages (Number) Deaths - Alcohol use disorders - Sex: Both - Age: All Ages (Number) Deaths - Exposure to forces of nature - Sex: Both - Age: All Ages (Number) Deaths - Diarrheal diseases - Sex: Both - Age: All Ages (Number) Deaths - Environmental heat and cold exposure - Sex: Both - Age: All Ages (Number) Deaths - Nutritional deficiencies - Sex: Both - Age: All Ages (Number) Deaths - Self-harm - Sex: Both - Age: All Ages (Number) Deaths - Conflict and terrorism - Sex: Both - Age: All Ages (Number) Deaths - Diabetes mellitus - Sex: Both - Age: All Ages (Number) Deaths - Poisonings - Sex: Both - Age: All Ages (Number) Deaths - Protein-energy malnutrition - Sex: Both - Age: All Ages (Number) Terrorism (deaths) Deaths - Cardiovascular diseases - Sex: Both - Age: All Ages (Number) Deaths - Chronic kidney disease - Sex: Both - Age: All Ages (Number) Deaths - Chronic respiratory diseases - Sex: Both - Age: All Ages (Number) Deaths - Cirrhosis and other chronic liver diseases - Sex: Both - Age: All Ages (Number) Deaths - Digestive diseases - Sex: Both - Age: All Ages (Number) Deaths - Acute hepatitis - Sex: Both - Age: All Ages (Number) Deaths - Alzheimer's disease and other dementias - Sex: Both - Age: All Ages (Number) Deaths - Parkinson's disease - Sex: Both - Age: All Ages (Number)
0 Afghanistan AFG 2007 15 2933.0 15925.0 481.0 393.0 2127.0 3657.0 148.0 252.0 4995.0 7425.0 4990.0 27672.0 23890.0 111.0 296.0 9320.0 57.0 2488.0 1310.0 8220.0 3189.0 513.0 2439.0 1199.0 53962.0 4490.0 7222.0 3346.0 6458.0 3437.0 1402.0 450.0
1 Afghanistan AFG 2008 17 2731.0 16148.0 462.0 255.0 1973.0 3785.0 157.0 261.0 4790.0 7355.0 5020.0 25800.0 23792.0 114.0 1317.0 8275.0 57.0 2277.0 1330.0 6895.0 3261.0 495.0 2231.0 1092.0 54051.0 4534.0 7143.0 3316.0 6408.0 3005.0 1424.0 455.0
In [ ]:
In [89]:
#Renaming column headers
df.rename(columns = {'Entity': 'Country', 'Code': 'Country Code', 'Year': 'Year_of_Death',
                     'Number of executions (Amnesty International)':'Executions_Amty_Int', 
                     'Deaths - Meningitis - Sex: Both - Age: All Ages (Number)': 'Meningitis',
                     'Deaths - Neoplasms - Sex: Both - Age: All Ages (Number)': 'Neoplasms',
                     'Deaths - Fire, heat, and hot substances - Sex: Both - Age: All Ages (Number)': 'Fire_heat_and_hot_substances',
                     'Deaths - Malaria - Sex: Both - Age: All Ages (Number)': 'Malaria', 
                     'Deaths - Drowning - Sex: Both - Age: All Ages (Number)': 'Drowning', 
                     'Deaths - Interpersonal violence - Sex: Both - Age: All Ages (Number)': 'Interpersonal_violence', 
                     'Deaths - HIV/AIDS - Sex: Both - Age: All Ages (Number)': 'HIV/AIDS', 
                     'Deaths - Drug use disorders - Sex: Both - Age: All Ages (Number)': 'Drug_use_disorders',
                     'Deaths - Tuberculosis - Sex: Both - Age: All Ages (Number)': 'Tuberculosis', 
                     'Deaths - Road injuries - Sex: Both - Age: All Ages (Number)': 'Road_injuries',
                     'Deaths - Maternal disorders - Sex: Both - Age: All Ages (Number)': 'Maternal_disorders', 
                     'Deaths - Lower respiratory infections - Sex: Both - Age: All Ages (Number)': 'Lower_respiratory_infections',
                     'Deaths - Neonatal disorders - Sex: Both - Age: All Ages (Number)': 'Neonatal_disorders', 
                     'Deaths - Alcohol use disorders - Sex: Both - Age: All Ages (Number)': 'Alcohol_use_disorders', 
                     'Deaths - Exposure to forces of nature - Sex: Both - Age: All Ages (Number': 'Exposure_to_forces_of_nature',
                     'Deaths - Diarrheal diseases - Sex: Both - Age: All Ages (Number)': 'Diarrheal_diseases', 
                     'Deaths - Environmental heat and cold exposure - Sex: Both - Age: All Ages (Number)': 'Environmental_heat_and_cold_exposure',
                     'Deaths - Nutritional deficiencies - Sex: Both - Age: All Ages (Number)': 'Nutritional_deficiencies',
                     'Deaths - Self-harm - Sex: Both - Age: All Ages (Number)': 'Self-harm',
                     'Deaths - Conflict and terrorism - Sex: Both - Age: All Ages (Number)': 'Conflict_and_terrorism', 
                     'Deaths - Diabetes mellitus - Sex: Both - Age: All Ages (Number': 'Diabetes_Mellitus',
                     'Deaths - Poisonings - Sex: Both - Age: All Ages (Number)': 'Poisonings', 
                     'Deaths - Protein-energy malnutrition - Sex: Both - Age: All Ages (Number)': 'Protein-energy_malnutrition',
                     'Terrorism (deaths)': 'Terrorism',
                     'Deaths - Cardiovascular diseases - Sex: Both - Age: All Ages (Number)': 'Cardiovascular_diseases', 
                     'Deaths - Chronic kidney disease - Sex: Both - Age: All Ages (Number)': 'Chronic_kidney_disease', 
                     'Deaths - Chronic respiratory diseases - Sex: Both - Age: All Ages (Number)': 'Deaths - Chronic_Respiratory_diseases',
                     'Deaths - Cirrhosis and other chronic liver diseases - Sex: Both - Age: All Ages (Number)': 'Cirrhosis_liver_diseases', 
                     'Deaths - Digestive diseases - Sex: Both - Age: All Ages (Number)': 'Digestive_diseases',
                     'Deaths - Acute hepatitis - Sex: Both - Age: All Ages (Number)': 'Acute_hepatitis', 
                     "Deaths - Alzheimer's disease and other dementias - Sex: Both - Age: All Ages (Number)": 'Alzheimer disease',
                     "Deaths - Parkinson's disease - Sex: Both - Age: All Ages (Number)": 'Parkinson disease'}, inplace = True)
In [90]:
df.rename(columns = {'Deaths - Exposure to forces of nature - Sex: Both - Age: All Ages (Number)': 'Exposure_to_forces_of_nature', 
                     'Deaths - Diabetes mellitus - Sex: Both - Age: All Ages (Number)': 'Diabetes_mellitus', 
                     'Deaths - Chronic_Respiratory_diseases': 'Chronic_Respiratory_diseases'}, inplace = True)
In [91]:
#Creating a copy of the dataframe
new_df = df[:]
In [92]:
#To display the first five rows of the dataframe
new_df.head()
Out[92]:
Country Country Code Year_of_Death Executions_Amty_Int Meningitis Neoplasms Fire_heat_and_hot_substances Malaria Drowning Interpersonal_violence HIV/AIDS Drug_use_disorders Tuberculosis Road_injuries Maternal_disorders Lower_respiratory_infections Neonatal_disorders Alcohol_use_disorders Exposure_to_forces_of_nature Diarrheal_diseases Environmental_heat_and_cold_exposure Nutritional_deficiencies Self-harm Conflict_and_terrorism Diabetes_mellitus Poisonings Protein-energy_malnutrition Terrorism Cardiovascular_diseases Chronic_kidney_disease Chronic_Respiratory_diseases Cirrhosis_liver_diseases Digestive_diseases Acute_hepatitis Alzheimer disease Parkinson disease
0 Afghanistan AFG 2007 15 2933.0 15925.0 481.0 393.0 2127.0 3657.0 148.0 252.0 4995.0 7425.0 4990.0 27672.0 23890.0 111.0 296.0 9320.0 57.0 2488.0 1310.0 8220.0 3189.0 513.0 2439.0 1199.0 53962.0 4490.0 7222.0 3346.0 6458.0 3437.0 1402.0 450.0
1 Afghanistan AFG 2008 17 2731.0 16148.0 462.0 255.0 1973.0 3785.0 157.0 261.0 4790.0 7355.0 5020.0 25800.0 23792.0 114.0 1317.0 8275.0 57.0 2277.0 1330.0 6895.0 3261.0 495.0 2231.0 1092.0 54051.0 4534.0 7143.0 3316.0 6408.0 3005.0 1424.0 455.0
2 Afghanistan AFG 2009 0 2460.0 16383.0 448.0 239.0 1852.0 3874.0 167.0 270.0 4579.0 7290.0 5013.0 24340.0 23672.0 115.0 101.0 7359.0 57.0 2040.0 1342.0 7617.0 3336.0 483.0 1998.0 1065.0 53964.0 4597.0 7045.0 3291.0 6358.0 2663.0 1449.0 460.0
3 Afghanistan AFG 2011 2 2327.0 17094.0 448.0 390.0 1775.0 4170.0 184.0 292.0 4259.0 7432.0 4857.0 22883.0 23951.0 120.0 83.0 6412.0 58.0 1846.0 1391.0 9142.0 3550.0 483.0 1805.0 1525.0 54347.0 4785.0 6916.0 3318.0 6370.0 2365.0 1508.0 473.0
4 Afghanistan AFG 2012 14 2254.0 17522.0 445.0 94.0 1716.0 4245.0 191.0 305.0 4122.0 7494.0 4736.0 22162.0 24057.0 123.0 333.0 6008.0 103.0 1705.0 1413.0 11350.0 3682.0 482.0 1667.0 3521.0 54868.0 4846.0 6878.0 3353.0 6398.0 2264.0 1544.0 482.0
In [93]:
#To show the metadata of the dataframe
new_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 8254 entries, 0 to 8253
Data columns (total 36 columns):
 #   Column                                Non-Null Count  Dtype  
---  ------                                --------------  -----  
 0   Country                               8254 non-null   object 
 1   Country Code                          6206 non-null   object 
 2   Year_of_Death                         8254 non-null   int64  
 3   Executions_Amty_Int                   267 non-null    object 
 4   Meningitis                            8010 non-null   float64
 5   Neoplasms                             8010 non-null   float64
 6   Fire_heat_and_hot_substances          8010 non-null   float64
 7   Malaria                               8010 non-null   float64
 8   Drowning                              8010 non-null   float64
 9   Interpersonal_violence                8010 non-null   float64
 10  HIV/AIDS                              8010 non-null   float64
 11  Drug_use_disorders                    8010 non-null   float64
 12  Tuberculosis                          8010 non-null   float64
 13  Road_injuries                         8010 non-null   float64
 14  Maternal_disorders                    8010 non-null   float64
 15  Lower_respiratory_infections          8010 non-null   float64
 16  Neonatal_disorders                    8010 non-null   float64
 17  Alcohol_use_disorders                 8010 non-null   float64
 18  Exposure_to_forces_of_nature          8010 non-null   float64
 19  Diarrheal_diseases                    8010 non-null   float64
 20  Environmental_heat_and_cold_exposure  8010 non-null   float64
 21  Nutritional_deficiencies              8010 non-null   float64
 22  Self-harm                             8010 non-null   float64
 23  Conflict_and_terrorism                8010 non-null   float64
 24  Diabetes_mellitus                     8010 non-null   float64
 25  Poisonings                            8010 non-null   float64
 26  Protein-energy_malnutrition           8010 non-null   float64
 27  Terrorism                             2891 non-null   float64
 28  Cardiovascular_diseases               8010 non-null   float64
 29  Chronic_kidney_disease                8010 non-null   float64
 30  Chronic_Respiratory_diseases          8010 non-null   float64
 31  Cirrhosis_liver_diseases              8010 non-null   float64
 32  Digestive_diseases                    8010 non-null   float64
 33  Acute_hepatitis                       8010 non-null   float64
 34  Alzheimer disease                     8010 non-null   float64
 35  Parkinson disease                     8010 non-null   float64
dtypes: float64(32), int64(1), object(3)
memory usage: 2.3+ MB
In [94]:
#A transposed statistical display of the dataset
new_df.describe().T
Out[94]:
count mean std min 25% 50% 75% max
Year_of_Death 8254.0 2004.448025 8.642230e+00 1990.0 1997.00 2004.0 2012.00 2019.0
Meningitis 8010.0 12909.701124 4.179939e+04 0.0 29.00 294.0 3187.75 432524.0
Neoplasms 8010.0 298398.509363 8.643901e+05 1.0 1934.25 10338.5 91869.25 10079637.0
Fire_heat_and_hot_substances 8010.0 4444.838077 1.211191e+04 0.0 35.00 244.0 1470.75 129705.0
Malaria 8010.0 31812.044569 1.230359e+05 0.0 0.00 1.0 2462.00 961129.0
Drowning 8010.0 12532.637953 4.009599e+04 0.0 58.00 393.5 3017.75 460665.0
Interpersonal_violence 8010.0 15315.848315 4.288854e+04 0.0 76.25 494.0 4372.50 463129.0
HIV/AIDS 8010.0 47251.428215 1.744798e+05 0.0 26.00 420.0 9484.50 1844490.0
Drug_use_disorders 8010.0 3469.958926 1.118651e+04 0.0 7.00 57.0 518.75 128083.0
Tuberculosis 8010.0 56055.270162 1.837876e+05 0.0 62.00 956.0 10377.75 1808478.0
Road_injuries 8010.0 44516.614232 1.269077e+05 0.0 332.25 1969.5 13236.00 1285039.0
Maternal_disorders 8010.0 9317.366417 3.066556e+04 0.0 8.00 163.0 1868.00 302586.0
Lower_respiratory_infections 8010.0 104292.072409 2.897865e+05 0.0 644.50 5872.0 37384.25 3320008.0
Neonatal_disorders 8010.0 92688.577528 2.947175e+05 0.0 199.00 2344.0 20780.25 3005945.0
Alcohol_use_disorders 8010.0 6106.161174 1.845507e+04 0.0 22.00 172.0 1246.25 181768.0
Exposure_to_forces_of_nature 8010.0 1708.418352 1.383435e+04 0.0 0.00 2.0 99.00 248861.0
Diarrheal_diseases 8010.0 81460.836080 2.843796e+05 0.0 49.00 992.5 13289.25 2904396.0
Environmental_heat_and_cold_exposure 8010.0 2237.055930 7.179057e+03 0.0 5.00 55.0 321.00 72653.0
Nutritional_deficiencies 8010.0 16586.057303 5.546863e+04 0.0 15.00 291.0 4639.00 757152.0
Self-harm 8010.0 30072.524469 8.722992e+04 0.0 181.00 995.0 7478.00 841164.0
Conflict_and_terrorism 8010.0 3763.654432 2.298289e+04 0.0 0.00 3.0 338.75 566518.0
Diabetes_mellitus 8010.0 39436.227840 1.109832e+05 1.0 409.00 1894.0 14308.25 1551170.0
Poisonings 8010.0 3189.111111 9.180095e+03 0.0 13.00 125.0 797.75 92101.0
Protein-energy_malnutrition 8010.0 14441.384519 4.798772e+04 0.0 10.00 233.5 4245.00 656314.0
Terrorism 2891.0 349.235905 1.917144e+03 0.0 0.00 5.0 60.00 44490.0
Cardiovascular_diseases 8010.0 567277.738951 1.606918e+06 4.0 4348.50 23265.5 166331.75 18562510.0
Chronic_kidney_disease 8010.0 36145.452934 1.028788e+05 0.0 281.00 1651.0 11921.75 1427232.0
Chronic_Respiratory_diseases 8010.0 131501.249189 4.174924e+05 1.0 526.25 2960.5 28156.50 3974315.0
Cirrhosis_liver_diseases 8010.0 46686.335206 1.282383e+05 0.0 304.00 2134.0 16802.25 1472012.0
Digestive_diseases 8010.0 82614.909613 2.253554e+05 0.0 599.00 4032.5 28388.75 2557689.0
Acute_hepatitis 8010.0 4586.226592 1.669243e+04 0.0 3.00 47.0 453.75 166405.0
Alzheimer disease 8010.0 39233.946567 1.179772e+05 0.0 201.00 1337.0 11867.75 1623276.0
Parkinson disease 8010.0 9367.016979 2.735872e+04 0.0 55.00 331.0 2954.00 362907.0
In [95]:
new_df.shape
Out[95]:
(8254, 36)
In [96]:
#To display all columns with null values
new_df.isnull().sum()
Out[96]:
Country                                    0
Country Code                            2048
Year_of_Death                              0
Executions_Amty_Int                     7987
Meningitis                               244
Neoplasms                                244
Fire_heat_and_hot_substances             244
Malaria                                  244
Drowning                                 244
Interpersonal_violence                   244
HIV/AIDS                                 244
Drug_use_disorders                       244
Tuberculosis                             244
Road_injuries                            244
Maternal_disorders                       244
Lower_respiratory_infections             244
Neonatal_disorders                       244
Alcohol_use_disorders                    244
Exposure_to_forces_of_nature             244
Diarrheal_diseases                       244
Environmental_heat_and_cold_exposure     244
Nutritional_deficiencies                 244
Self-harm                                244
Conflict_and_terrorism                   244
Diabetes_mellitus                        244
Poisonings                               244
Protein-energy_malnutrition              244
Terrorism                               5363
Cardiovascular_diseases                  244
Chronic_kidney_disease                   244
Chronic_Respiratory_diseases             244
Cirrhosis_liver_diseases                 244
Digestive_diseases                       244
Acute_hepatitis                          244
Alzheimer disease                        244
Parkinson disease                        244
dtype: int64
In [ ]:
In [ ]:
In [97]:
# To dispaly all integer columns
new_df_int = []
for x in new_df.dtypes.index:
    if new_df.dtypes[x] == 'int64':
        new_df_int.append(x)
        
new_df_int
Out[97]:
['Year_of_Death']
In [ ]:
In [ ]:
In [98]:
# To dispaly all float64 columns
new_df_int = []
for x in new_df.dtypes.index:
    if new_df.dtypes[x] == 'float':
        new_df_int.append(x)
        
new_df_int
Out[98]:
['Meningitis',
 'Neoplasms',
 'Fire_heat_and_hot_substances',
 'Malaria',
 'Drowning',
 'Interpersonal_violence',
 'HIV/AIDS',
 'Drug_use_disorders',
 'Tuberculosis',
 'Road_injuries',
 'Maternal_disorders',
 'Lower_respiratory_infections',
 'Neonatal_disorders',
 'Alcohol_use_disorders',
 'Exposure_to_forces_of_nature',
 'Diarrheal_diseases',
 'Environmental_heat_and_cold_exposure',
 'Nutritional_deficiencies',
 'Self-harm',
 'Conflict_and_terrorism',
 'Diabetes_mellitus',
 'Poisonings',
 'Protein-energy_malnutrition',
 'Terrorism',
 'Cardiovascular_diseases',
 'Chronic_kidney_disease',
 'Chronic_Respiratory_diseases',
 'Cirrhosis_liver_diseases',
 'Digestive_diseases',
 'Acute_hepatitis',
 'Alzheimer disease',
 'Parkinson disease']
In [99]:
# To dispaly all object columns
new_df_int = []
for x in new_df.dtypes.index:
    if new_df.dtypes[x] == 'object':
        new_df_int.append(x)
        
new_df_int
Out[99]:
['Country', 'Country Code', 'Executions_Amty_Int']
In [112]:
#Dropping two columns
new_df.drop(columns = ['Executions_Amty_Int', 'Country Code'], inplace = True)
In [ ]:
In [119]:
#Replacing a null values with the mean of the dataframe
new_df.fillna(new_df.mean(),inplace = True)

C:\Users\user\AppData\Local\Temp/ipykernel_8456/4268064738.py:1: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.
  new_df.fillna(new_df.mean(),inplace = True)
In [121]:
#To verify there are no other null values in the dataframe
new_df.isna().sum()
Out[121]:
Country                                 0
Year_of_Death                           0
Meningitis                              0
Neoplasms                               0
Fire_heat_and_hot_substances            0
Malaria                                 0
Drowning                                0
Interpersonal_violence                  0
HIV/AIDS                                0
Drug_use_disorders                      0
Tuberculosis                            0
Road_injuries                           0
Maternal_disorders                      0
Lower_respiratory_infections            0
Neonatal_disorders                      0
Alcohol_use_disorders                   0
Exposure_to_forces_of_nature            0
Diarrheal_diseases                      0
Environmental_heat_and_cold_exposure    0
Nutritional_deficiencies                0
Self-harm                               0
Conflict_and_terrorism                  0
Diabetes_mellitus                       0
Poisonings                              0
Protein-energy_malnutrition             0
Terrorism                               0
Cardiovascular_diseases                 0
Chronic_kidney_disease                  0
Chronic_Respiratory_diseases            0
Cirrhosis_liver_diseases                0
Digestive_diseases                      0
Acute_hepatitis                         0
Alzheimer disease                       0
Parkinson disease                       0
dtype: int64
In [128]:
new_df.pivot_table(index = 'Year_of_Death', values = ['Meningitis', 'Neoplasms',
                                                      'Fire_heat_and_hot_substances', 'Malaria', 'Drowning', 'Interpersonal_violence', 'HIV/AIDS', 'Drug_use_disorders',
       'Tuberculosis', 'Road_injuries', 'Maternal_disorders',
       'Lower_respiratory_infections', 'Neonatal_disorders',
       'Alcohol_use_disorders', 'Exposure_to_forces_of_nature',
       'Diarrheal_diseases', 'Environmental_heat_and_cold_exposure',
       'Nutritional_deficiencies', 'Self-harm', 'Conflict_and_terrorism',
       'Diabetes_mellitus', 'Poisonings', 'Protein-energy_malnutrition',
       'Terrorism', 'Cardiovascular_diseases', 'Chronic_kidney_disease',
       'Chronic_Respiratory_diseases', 'Cirrhosis_liver_diseases',
       'Digestive_diseases', 'Acute_hepatitis', 'Alzheimer disease',
       'Parkinson disease'], aggfunc = 'sum')
Out[128]:
Acute_hepatitis Alcohol_use_disorders Alzheimer disease Cardiovascular_diseases Chronic_Respiratory_diseases Chronic_kidney_disease Cirrhosis_liver_diseases Conflict_and_terrorism Diabetes_mellitus Diarrheal_diseases Digestive_diseases Drowning Drug_use_disorders Environmental_heat_and_cold_exposure Exposure_to_forces_of_nature Fire_heat_and_hot_substances HIV/AIDS Interpersonal_violence Lower_respiratory_infections Malaria Maternal_disorders Meningitis Neonatal_disorders Neoplasms Nutritional_deficiencies Parkinson disease Poisonings Protein-energy_malnutrition Road_injuries Self-harm Terrorism Tuberculosis
Year_of_Death
1990 1.661965e+06 1.249543e+06 6.393869e+06 1.308505e+08 3.258818e+07 6.421137e+06 1.067155e+07 1.110766e+06 7.158245e+06 2.925154e+07 1.964008e+07 4.621206e+06 6.145225e+05 576158.615231 4.372296e+05 1.265927e+06 4.007790e+06 3.796059e+06 3.393282e+07 8.758178e+06 3.023030e+06 4.370912e+06 2.994935e+07 6.368086e+07 7.482045e+06 1.651046e+06 898864.222222 6.471534e+06 1.151764e+07 7.875844e+06 84655.698720 1.797335e+07
1991 1.657380e+06 1.316474e+06 6.673846e+06 1.328985e+08 3.327618e+07 6.581273e+06 1.086276e+07 8.387149e+05 7.382670e+06 2.943951e+07 1.995623e+07 4.575059e+06 6.770935e+05 593810.671161 1.436020e+06 1.278711e+06 5.023239e+06 3.912932e+06 3.371600e+07 8.979938e+06 2.993140e+06 4.350372e+06 2.976181e+07 6.515604e+07 7.237208e+06 1.698855e+06 900314.333333 6.249857e+06 1.159941e+07 8.042053e+06 76624.677966 1.813422e+07
1992 1.646589e+06 1.414127e+06 6.955305e+06 1.354871e+08 3.399616e+07 6.778986e+06 1.106688e+07 6.285105e+05 7.643306e+06 2.921887e+07 2.029752e+07 4.514849e+06 7.311735e+05 628736.727091 1.362854e+05 1.292700e+06 6.185702e+06 4.153043e+06 3.360680e+07 9.003701e+06 3.010785e+06 4.341994e+06 2.961701e+07 6.674348e+07 6.978514e+06 1.748998e+06 909005.444444 6.022944e+06 1.171822e+07 8.245211e+06 73229.723971 1.840361e+07
1993 1.569312e+06 1.456631e+06 6.697687e+06 1.316369e+08 3.287207e+07 6.477967e+06 1.070366e+07 5.721000e+05 7.384169e+06 2.743239e+07 1.957938e+07 4.335471e+06 7.349480e+05 668640.000000 2.031900e+05 1.259350e+06 6.841321e+06 4.199189e+06 3.197443e+07 8.656775e+06 2.830872e+06 4.118829e+06 2.816487e+07 6.459464e+07 6.507485e+06 1.682132e+06 882970.000000 5.622720e+06 1.124102e+07 8.064378e+06 93245.986510 1.733862e+07
1994 1.591217e+06 1.617405e+06 7.330315e+06 1.393954e+08 3.448724e+07 7.019021e+06 1.138206e+07 6.143654e+06 8.005891e+06 2.768482e+07 2.068427e+07 4.439960e+06 8.222436e+05 745401.559301 1.379752e+05 1.325004e+06 8.701827e+06 4.443971e+06 3.265090e+07 8.916309e+06 2.919340e+06 4.178250e+06 2.882937e+07 6.887554e+07 6.437942e+06 1.817816e+06 923220.111111 5.576330e+06 1.183842e+07 8.618473e+06 67772.195780 1.769225e+07
1995 1.574135e+06 1.651731e+06 7.610644e+06 1.409082e+08 3.478288e+07 7.239235e+06 1.157666e+07 6.763032e+05 8.263913e+06 2.711182e+07 2.093490e+07 4.391703e+06 8.594585e+05 750927.615231 2.084336e+05 1.317046e+06 1.016770e+07 4.470921e+06 3.241442e+07 9.021580e+06 2.901961e+06 4.151242e+06 2.870609e+07 7.026983e+07 7.098007e+06 1.874415e+06 920443.222222 6.271585e+06 1.196564e+07 8.726806e+06 67202.026634 1.760259e+07
1996 1.537720e+06 1.648099e+06 7.840580e+06 1.416507e+08 3.508088e+07 7.448282e+06 1.168176e+07 8.476532e+05 8.518086e+06 2.656425e+07 2.104785e+07 4.252830e+06 8.779755e+05 700090.615231 1.623596e+05 1.299951e+06 1.142593e+07 4.349887e+06 3.187989e+07 9.126420e+06 2.875882e+06 4.236686e+06 2.840057e+07 7.115734e+07 6.619371e+06 1.925851e+06 905442.222222 5.830991e+06 1.195681e+07 8.664227e+06 70489.498443 1.743498e+07
1997 1.519494e+06 1.633219e+06 8.017137e+06 1.422615e+08 3.535266e+07 7.679628e+06 1.177324e+07 9.533525e+05 8.770191e+06 2.614563e+07 2.116454e+07 4.145914e+06 8.851156e+05 660825.559301 1.814962e+05 1.289603e+06 1.253207e+07 4.282593e+06 3.137905e+07 9.305505e+06 2.886995e+06 4.054495e+06 2.809002e+07 7.184094e+07 6.368869e+06 1.973769e+06 887887.111111 5.610298e+06 1.198785e+07 8.661926e+06 83411.206157 1.744728e+07
1998 1.488658e+06 1.630028e+06 8.161538e+06 1.422553e+08 3.529417e+07 7.885472e+06 1.180409e+07 1.030349e+06 8.957464e+06 2.565834e+07 2.116481e+07 4.062067e+06 9.052717e+05 667525.447441 4.385733e+05 1.266444e+06 1.379532e+07 4.292973e+06 3.072340e+07 9.326665e+06 2.869754e+06 3.957349e+06 2.768783e+07 7.245530e+07 6.071071e+06 2.015003e+06 874906.888889 5.359881e+06 1.197872e+07 8.674909e+06 79720.350052 1.733040e+07
1999 1.464686e+06 1.673449e+06 8.399478e+06 1.447198e+08 3.548634e+07 8.154977e+06 1.197172e+07 1.334037e+06 9.208637e+06 2.520534e+07 2.140567e+07 3.965088e+06 9.301717e+05 652485.447441 6.429963e+05 1.277764e+06 1.523411e+07 4.375273e+06 3.026056e+07 9.261324e+06 2.868104e+06 3.925535e+06 2.743691e+07 7.403033e+07 5.807843e+06 2.082333e+06 881250.888889 5.145720e+06 1.214743e+07 8.781565e+06 70596.811484 1.728829e+07
2000 1.446091e+06 1.740665e+06 8.737762e+06 1.480113e+08 3.604749e+07 8.524581e+06 1.224904e+07 1.135916e+06 9.550049e+06 2.482737e+07 2.182162e+07 3.897650e+06 9.620536e+05 677036.559301 1.033542e+05 1.293796e+06 1.660532e+07 4.492312e+06 2.992586e+07 9.206590e+06 2.870678e+06 3.914298e+06 2.736994e+07 7.606776e+07 5.599695e+06 2.172943e+06 902435.111111 4.967193e+06 1.240298e+07 8.821783e+06 71182.160152 1.736304e+07
2001 1.403053e+06 1.782497e+06 8.989578e+06 1.495699e+08 3.607339e+07 8.769196e+06 1.242373e+07 6.534649e+05 9.798067e+06 2.415664e+07 2.202179e+07 3.771793e+06 9.361156e+05 687579.503371 3.028338e+05 1.287404e+06 1.766627e+07 4.481228e+06 2.915766e+07 9.493590e+06 2.817243e+06 3.842552e+06 2.700187e+07 7.689816e+07 5.333007e+06 2.243045e+06 904217.000000 4.733175e+06 1.246854e+07 8.604734e+06 81160.160152 1.699420e+07
2002 1.352192e+06 1.828438e+06 9.314889e+06 1.525915e+08 3.632446e+07 9.083340e+06 1.269217e+07 6.270769e+05 1.015371e+07 2.352020e+07 2.238626e+07 3.636232e+06 9.119396e+05 719415.503371 9.812677e+04 1.278814e+06 1.858488e+07 4.573952e+06 2.875622e+07 9.654239e+06 2.756833e+06 3.769457e+06 2.682721e+07 7.831447e+07 5.094432e+06 2.334243e+06 910720.000000 4.532484e+06 1.258816e+07 8.508447e+06 79722.114147 1.667683e+07
2003 1.305056e+06 1.865017e+06 9.641248e+06 1.545311e+08 3.634466e+07 9.364362e+06 1.292875e+07 6.629229e+05 1.044869e+07 2.284575e+07 2.268449e+07 3.498296e+06 8.887496e+05 742497.503371 3.116288e+05 1.282700e+06 1.927681e+07 4.441960e+06 2.834549e+07 9.942037e+06 2.690640e+06 3.738645e+06 2.663623e+07 7.967422e+07 4.173177e+06 2.410932e+06 918305.000000 3.651981e+06 1.269105e+07 8.542525e+06 75258.114147 1.631525e+07
2004 1.274595e+06 1.869142e+06 9.894721e+06 1.534706e+08 3.577863e+07 9.495341e+06 1.297099e+07 6.455126e+05 1.053445e+07 2.194370e+07 2.264725e+07 3.383649e+06 8.791027e+05 687161.391511 2.445689e+06 1.252563e+06 1.958029e+07 4.359170e+06 2.764228e+07 9.877015e+06 2.633312e+06 3.659508e+06 2.623107e+07 8.021770e+07 3.916311e+06 2.447128e+06 920138.777778 3.430216e+06 1.269446e+07 8.525595e+06 89520.832238 1.567580e+07
2005 1.291444e+06 1.900321e+06 1.039329e+07 1.575186e+08 3.634158e+07 9.941602e+06 1.338061e+07 5.490365e+05 1.097457e+07 2.179459e+07 2.327692e+07 3.367305e+06 9.231846e+05 706402.559301 8.285672e+05 1.271783e+06 1.964079e+07 4.358761e+06 2.777185e+07 9.767443e+06 2.653198e+06 3.683544e+06 2.625414e+07 8.261733e+07 3.799404e+06 2.566524e+06 936083.111111 3.321915e+06 1.292725e+07 8.683788e+06 87792.473193 1.559020e+07
2006 1.248982e+06 1.849987e+06 1.069656e+07 1.562081e+08 3.589888e+07 1.012354e+07 1.334632e+07 6.572452e+05 1.108847e+07 2.149156e+07 2.320262e+07 3.218064e+06 9.224287e+05 665131.447441 2.047783e+05 1.235509e+06 1.890123e+07 4.239392e+06 2.725773e+07 9.720416e+06 2.585376e+06 3.636044e+06 2.574857e+07 8.272365e+07 3.632662e+06 2.612923e+06 908342.888889 3.165333e+06 1.281269e+07 8.503527e+06 94844.057765 1.515347e+07
2007 1.217801e+06 1.825958e+06 1.118838e+07 1.584827e+08 3.622814e+07 1.048454e+07 1.355200e+07 6.420895e+05 1.137690e+07 2.134275e+07 2.352770e+07 3.165186e+06 9.359986e+05 620949.559301 1.671282e+05 1.228440e+06 1.797353e+07 4.192174e+06 2.718860e+07 9.772339e+06 2.537653e+06 3.602614e+06 2.557652e+07 8.462961e+07 3.523017e+06 2.712474e+06 901256.111111 3.060479e+06 1.294596e+07 8.447368e+06 103429.878243 1.497545e+07
2008 1.186935e+06 1.803678e+06 1.163707e+07 1.612115e+08 3.666256e+07 1.083906e+07 1.373251e+07 6.137695e+05 1.166276e+07 2.103084e+07 2.381166e+07 3.097459e+06 9.486146e+05 600800.559301 2.228889e+06 1.214602e+06 1.682126e+07 4.212860e+06 2.704886e+07 9.718697e+06 2.491211e+06 3.512374e+06 2.525785e+07 8.641431e+07 3.409644e+06 2.815241e+06 903574.111111 2.951052e+06 1.303947e+07 8.399150e+06 87539.575579 1.475146e+07
2009 1.132843e+06 1.744117e+06 1.201470e+07 1.614772e+08 3.627802e+07 1.104654e+07 1.363317e+07 6.824132e+05 1.177254e+07 2.020299e+07 2.363852e+07 2.995919e+06 9.426537e+05 567654.447441 1.186943e+05 1.185314e+06 1.560973e+07 4.169610e+06 2.652408e+07 9.503830e+06 2.435246e+06 3.426390e+06 2.468162e+07 8.722632e+07 3.255591e+06 2.872161e+06 889897.888889 2.814886e+06 1.291021e+07 8.223743e+06 90332.226911 1.422411e+07
2010 1.103009e+06 1.721060e+06 1.249982e+07 1.642387e+08 3.639871e+07 1.138289e+07 1.372950e+07 5.292852e+05 1.199310e+07 1.973709e+07 2.381923e+07 2.939151e+06 9.543587e+05 606374.447441 1.987123e+06 1.176412e+06 1.467533e+07 4.119327e+06 2.627671e+07 9.401187e+06 2.392538e+06 3.324390e+06 2.423807e+07 8.882902e+07 3.246251e+06 2.958818e+06 887901.888889 2.810585e+06 1.287015e+07 8.207513e+06 89137.350052 1.382533e+07
2011 1.073792e+06 1.692581e+06 1.297180e+07 1.662087e+08 3.670323e+07 1.174337e+07 1.380419e+07 5.723632e+05 1.230503e+07 1.946982e+07 2.399849e+07 2.845752e+06 9.690417e+05 535962.447441 3.429183e+05 1.160212e+06 1.381655e+07 4.083633e+06 2.611534e+07 8.980200e+06 2.342251e+06 3.186528e+06 2.380662e+07 9.023874e+07 3.173359e+06 3.045024e+06 872625.888889 2.739772e+06 1.274892e+07 8.132530e+06 89695.878243 1.349532e+07
2012 1.050203e+06 1.667714e+06 1.341407e+07 1.676963e+08 3.677806e+07 1.204165e+07 1.380502e+07 9.636986e+05 1.264148e+07 1.872744e+07 2.404462e+07 2.780783e+06 9.842797e+05 531958.391511 9.566293e+04 1.138845e+06 1.285580e+07 4.088396e+06 2.601730e+07 8.304909e+06 2.258981e+06 3.052961e+06 2.330111e+07 9.139334e+07 3.010665e+06 3.126003e+06 854664.777778 2.581216e+06 1.257483e+07 7.997492e+06 108305.755102 1.330962e+07
2013 9.981518e+05 1.668957e+06 1.393847e+07 1.706409e+08 3.743920e+07 1.245557e+07 1.389226e+07 1.023420e+06 1.307296e+07 1.834886e+07 2.431654e+07 2.707591e+06 1.027975e+06 515092.447441 2.655973e+05 1.141500e+06 1.211971e+07 4.081944e+06 2.624264e+07 7.795629e+06 2.230907e+06 2.960438e+06 2.300817e+07 9.314492e+07 2.964201e+06 3.240934e+06 848172.888889 2.538171e+06 1.238418e+07 7.909681e+06 125141.396057 1.326555e+07
2014 9.376728e+05 1.674131e+06 1.444180e+07 1.728338e+08 3.784308e+07 1.278522e+07 1.392697e+07 1.626829e+06 1.348873e+07 1.772904e+07 2.445953e+07 2.626487e+06 1.080419e+06 506762.447441 8.380335e+04 1.124477e+06 1.155184e+07 4.061761e+06 2.620417e+07 7.532065e+06 2.145691e+06 2.873670e+06 2.241344e+07 9.477012e+07 2.897287e+06 3.336769e+06 832759.888889 2.475561e+06 1.220137e+07 7.798169e+06 190046.216534 1.314262e+07
2015 8.969278e+05 1.688641e+06 1.497279e+07 1.765291e+08 3.825057e+07 1.318309e+07 1.413279e+07 1.449403e+06 1.393661e+07 1.726607e+07 2.485794e+07 2.584297e+06 1.148310e+06 535081.447441 1.398883e+05 1.125022e+06 1.111296e+07 4.088260e+06 2.623585e+07 7.315673e+06 2.081780e+06 2.747332e+06 2.189841e+07 9.692326e+07 2.843148e+06 3.429082e+06 822652.888889 2.426802e+06 1.207455e+07 7.771952e+06 173484.452439 1.287277e+07
2016 8.522226e+05 1.696197e+06 1.551580e+07 1.789867e+08 3.861080e+07 1.351894e+07 1.426855e+07 1.370596e+06 1.434866e+07 1.679557e+07 2.511758e+07 2.518982e+06 1.227278e+06 492241.391511 9.431993e+04 1.117217e+06 1.074192e+07 4.046698e+06 2.597101e+07 6.860928e+06 2.027220e+06 2.671863e+06 2.099461e+07 9.864107e+07 2.771400e+06 3.509369e+06 809336.777778 2.361482e+06 1.195128e+07 7.740496e+06 158046.093393 1.263867e+07
2017 8.250854e+05 1.695006e+06 1.606750e+07 1.812624e+08 3.902449e+07 1.373698e+07 1.443222e+07 1.118332e+06 1.471771e+07 1.663643e+07 2.541115e+07 2.438215e+06 1.283179e+06 477866.335581 1.292675e+05 1.106364e+06 1.019817e+07 4.077815e+06 2.559143e+07 6.524143e+06 1.973958e+06 2.548515e+06 2.008372e+07 1.002749e+08 2.697389e+06 3.586867e+06 791345.666667 2.292121e+06 1.185473e+07 7.714167e+06 134514.272916 1.240577e+07
2018 7.823130e+05 1.685875e+06 1.643764e+07 1.821855e+08 3.915169e+07 1.387856e+07 1.436280e+07 8.765680e+05 1.493975e+07 1.561673e+07 2.528801e+07 2.348988e+06 1.312393e+06 464640.000000 1.092820e+05 1.094495e+06 9.337500e+06 4.008793e+06 2.497413e+07 6.367998e+06 1.908483e+06 2.387470e+06 1.883323e+07 1.011568e+08 2.508095e+06 3.654087e+06 765508.000000 2.120805e+06 1.166577e+07 7.648006e+06 93245.986510 1.180568e+07
2019 7.651880e+05 1.709565e+06 1.698870e+07 1.865919e+08 4.001690e+07 1.426023e+07 1.459103e+07 5.298210e+05 1.540983e+07 1.518179e+07 2.569630e+07 2.318444e+06 1.364993e+06 479449.000000 5.917300e+04 1.099726e+06 9.028405e+06 3.962124e+06 2.504127e+07 6.481284e+06 1.892483e+06 2.332419e+06 1.824529e+07 1.040213e+08 2.444227e+06 3.770569e+06 758680.000000 2.062098e+06 1.168243e+07 7.682558e+06 93245.986510 1.155348e+07
In [191]:
new_df.groupby(['Year_of_Death']).sum()['Meningitis'].sort_values(ascending = False)
Out[191]:
Year_of_Death
1990   4370911.7124
1991   4350372.4135
1992   4341994.1146
1996   4236685.7124
1994   4178250.0112
1995   4151241.7124
1993   4118829.0000
1997   4054495.0112
1998   3957348.6090
1999   3925534.6090
2000   3914298.0112
2001   3842552.3101
2002   3769457.3101
2003   3738645.3101
2005   3683544.0112
2004   3659507.9079
2006   3636043.6090
2007   3602614.0112
2008   3512374.0112
2009   3426389.6090
2010   3324389.6090
2011   3186527.6090
2012   3052960.9079
2013   2960437.6090
2014   2873669.6090
2015   2747331.6090
2016   2671862.9079
2017   2548515.2067
2018   2387470.0000
2019   2332419.0000
Name: Meningitis, dtype: float64
In [149]:
pd.options.display.float_format = '{:.4f}'.format
In [156]:
sns.barplot(data = new_df, x = 'Year_of_Death', y = 'Meningitis')
sns.set(rc = {'figure.figsize':(25,16)})
In [ ]:
In [146]:
import plotly.express as px
In [147]:
fig = px.bar(new_df, x = 'Year_of_Death', y = 'Meningitis', color_discrete_sequence = ['Gold', 'Silver', 'Brown'])
fig.show()
In [192]:
fig = px.bar(new_df, x = 'Year_of_Death', y = 'Meningitis', color = 'Meningitis')
fig.show()
In [166]:
#To confirm the corrctness of the plot
new_df[new_df['Year_of_Death'] == 2019]['Meningitis'].sum()
Out[166]:
2332419.0
In [193]:
new_df.groupby(['Year_of_Death']).sum()['Neoplasms'].sort_values(ascending = False)
Out[193]:
Year_of_Death
2019   104021332.0000
2018   101156797.0000
2017   100274878.0562
2016    98641069.5655
2015    96923257.0749
2014    94770121.0749
2013    93144920.0749
2012    91393340.5655
2011    90238742.0749
2010    88829022.0749
2009    87226316.0749
2008    86414311.0936
2007    84629606.0936
2006    82723654.0749
2005    82617330.0936
2004    80217695.5655
2003    79674216.5843
2002    78314470.5843
2001    76898160.5843
2000    76067762.0936
1999    74030326.0749
1998    72455300.0749
1997    71840940.0936
1996    71157339.6030
1995    70269834.6030
1994    68875535.0936
1992    66743476.6217
1991    65156037.1124
1993    64594642.0000
1990    63680862.6030
Name: Neoplasms, dtype: float64
In [174]:
fig = px.bar(new_df, x = 'Year_of_Death', y = 'Neoplasms', color = 'Neoplasms')
fig.show()
In [194]:
new_df.groupby(['Year_of_Death']).sum()['Fire_heat_and_hot_substances'].sort_values(ascending = False)
Out[194]:
Year_of_Death
1994   1325004.3808
1995   1317046.2189
1996   1299951.2189
2000   1293796.3808
1992   1292699.8950
1997   1289603.3808
2001   1287403.5427
2003   1282699.5427
2002   1278813.5427
1991   1278711.0569
1999   1277763.7046
2005   1271783.3808
1998   1266443.7046
1990   1265927.2189
1993   1259350.0000
2004   1252562.8665
2006   1235508.7046
2007   1228440.3808
2008   1214602.3808
2009   1185313.7046
2010   1176411.7046
2011   1160211.7046
2013   1141499.7046
2012   1138844.8665
2015   1125021.7046
2014   1124476.7046
2016   1117216.8665
2017   1106364.0285
2019   1099726.0000
2018   1094495.0000
Name: Fire_heat_and_hot_substances, dtype: float64
In [195]:
fig = px.bar(new_df, x = 'Year_of_Death', y = 'Fire_heat_and_hot_substances', color = 'Fire_heat_and_hot_substances')
fig.show()
In [196]:
new_df.groupby(['Year_of_Death']).sum()['Malaria'].sort_values(ascending = False)
Out[196]:
Year_of_Death
2003   9942037.4011
2004   9877015.3120
2007   9772339.4457
2005   9767443.4457
2006   9720416.3566
2008   9718697.4457
2002   9654239.4011
2009   9503830.3566
2001   9493590.4011
2010   9401187.3566
1998   9326665.3566
1997   9305505.4457
1999   9261324.3566
2000   9206590.4457
1996   9126420.4903
1995   9021580.4903
1992   9003700.5794
2011   8980200.3566
1991   8979937.5348
1994   8916309.4457
1990   8758178.4903
1993   8656775.0000
2012   8304909.3120
2013   7795629.3566
2014   7532065.3566
2015   7315673.3566
2016   6860928.3120
2017   6524143.2674
2019   6481284.0000
2018   6367998.0000
Name: Malaria, dtype: float64
In [197]:
fig = px.bar(new_df, x = 'Year_of_Death', y = 'Malaria', color = 'Malaria')
fig.show()
In [198]:
new_df.groupby(['Year_of_Death']).sum()['Drowning'].sort_values(ascending = False)
Out[198]:
Year_of_Death
1990   4621206.0175
1991   4575058.6554
1992   4514849.2934
1994   4439960.3795
1995   4391703.0175
1993   4335471.0000
1996   4252830.0175
1997   4145914.3795
1998   4062067.1036
1999   3965088.1036
2000   3897650.3795
2001   3771792.7416
2002   3636231.7416
2003   3498295.7416
2004   3383649.4657
2005   3367305.3795
2006   3218064.1036
2007   3165186.3795
2008   3097459.3795
2009   2995919.1036
2010   2939151.1036
2011   2845752.1036
2012   2780783.4657
2013   2707591.1036
2014   2626487.1036
2015   2584297.1036
2016   2518982.4657
2017   2438214.8277
2018   2348988.0000
2019   2318444.0000
Name: Drowning, dtype: float64
In [199]:
fig = px.bar(new_df, x = 'Year_of_Death', y = 'Drowning', color = 'Drowning')
fig.show()
In [200]:
new_df.groupby(['Year_of_Death']).sum()['Interpersonal_violence'].sort_values(ascending = False)
Out[200]:
Year_of_Death
2002   4573951.6348
2000   4492312.4831
2001   4481227.6348
1995   4470921.3315
1994   4443971.4831
2003   4441959.6348
1999   4375272.7865
2004   4359169.9382
2005   4358761.4831
1996   4349887.3315
1998   4292972.7865
1997   4282593.4831
2006   4239391.7865
2008   4212860.4831
1993   4199189.0000
2007   4192174.4831
2009   4169609.7865
1992   4153043.0281
2010   4119326.7865
2012   4088395.9382
2015   4088259.7865
2011   4083632.7865
2013   4081943.7865
2017   4077815.0899
2014   4061760.7865
2016   4046697.9382
2018   4008793.0000
2019   3962124.0000
1991   3912932.1798
1990   3796059.3315
Name: Interpersonal_violence, dtype: float64
In [201]:
fig = px.bar(new_df, x = 'Year_of_Death', y = 'Interpersonal_violence', color = 'Interpersonal_violence')
fig.show()
In [202]:
new_df.groupby(['Year_of_Death']).sum()['HIV/AIDS'].sort_values(ascending = False)
Out[202]:
Year_of_Death
2005   19640789.2821
2004   19580286.9975
2003   19276811.8539
2006   18901231.4257
2002   18584875.8539
2007   17973530.2821
2001   17666266.8539
2008   16821261.2821
2000   16605315.2821
2009   15609728.4257
1999   15234105.4257
2010   14675332.4257
2011   13816550.4257
1998   13795319.4257
2012   12855800.9975
1997   12532070.2821
2013   12119709.4257
2014   11551836.4257
1996   11425928.7104
2015   11112960.4257
2016   10741918.9975
2017   10198173.5693
1995   10167700.7104
2018    9337500.0000
2019    9028405.0000
1994    8701827.2821
1993    6841321.0000
1992    6185701.5668
1991    5023239.1386
1990    4007789.7104
Name: HIV/AIDS, dtype: float64
In [203]:
fig = px.bar(new_df, x = 'Year_of_Death', y = 'HIV/AIDS', color = 'HIV/AIDS')
fig.show()
In [204]:
new_df.groupby(['Year_of_Death']).sum()['Drug_use_disorders'].sort_values(ascending = False)
Out[204]:
Year_of_Death
2019   1364993.0000
2018   1312393.0000
2017   1283178.7536
2016   1227277.7125
2015   1148309.6714
2014   1080418.6714
2013   1027974.6714
2012    984279.7125
2011    969041.6714
2000    962053.5893
2010    954358.6714
2008    948614.5893
2009    942653.6714
2001    936115.6303
2007    935998.5893
1999    930171.6714
2005    923184.5893
2006    922428.6714
2002    911939.6303
1998    905271.6714
2003    888749.6303
1997    885115.5893
2004    879102.7125
1996    877975.5482
1995    859458.5482
1994    822243.5893
1993    734948.0000
1992    731173.4660
1991    677093.5071
1990    614522.5482
Name: Drug_use_disorders, dtype: float64
In [205]:
fig = px.bar(new_df, x = 'Year_of_Death', y = 'Drug_use_disorders', color = 'Drug_use_disorders')
fig.show()
In [206]:
new_df.groupby(['Year_of_Death']).sum()['Tuberculosis'].sort_values(ascending = False)
Out[206]:
Year_of_Death
1992   18403612.5121
1991   18134219.2419
1990   17973351.9718
1994   17692251.7016
1995   17602585.9718
1997   17447276.7016
1996   17434983.9718
2000   17363037.7016
1993   17338616.0000
1998   17330402.1613
1999   17288292.1613
2001   16994197.4315
2002   16676834.4315
2003   16315248.4315
2004   15675802.8911
2005   15590202.7016
2006   15153465.1613
2007   14975454.7016
2008   14751457.7016
2009   14224110.1613
2010   13825329.1613
2011   13495323.1613
2012   13309616.8911
2013   13265546.1613
2014   13142615.1613
2015   12872771.1613
2016   12638673.8911
2017   12405768.6210
2018   11805677.0000
2019   11553475.0000
Name: Tuberculosis, dtype: float64
In [207]:
fig = px.bar(new_df, x = 'Year_of_Death', y = 'Tuberculosis', color = 'Tuberculosis')
fig.show()
In [208]:
new_df.groupby(['Year_of_Death']).sum()['Road_injuries'].sort_values(ascending = False)
Out[208]:
Year_of_Death
2008   13039470.1423
2007   12945963.1423
2005   12927251.1423
2009   12910214.9139
2010   12870145.9139
2006   12812690.9139
2011   12748924.9139
2004   12694464.2996
2003   12691054.5281
2002   12588155.5281
2012   12574828.2996
2001   12468542.5281
2000   12402977.1423
2013   12384180.9139
2014   12201365.9139
1999   12147429.9139
2015   12074548.9139
1997   11987848.1423
1998   11978722.9139
1995   11965636.7566
1996   11956809.7566
2016   11951278.2996
2017   11854728.6854
1994   11838415.1423
1992   11718224.9850
2019   11682433.0000
2018   11665769.0000
1991   11599405.3708
1990   11517636.7566
1993   11241016.0000
Name: Road_injuries, dtype: float64
In [209]:
fig = px.bar(new_df, x = 'Year_of_Death', y = 'Road_injuries', color = 'Road_injuries')
fig.show()
In [210]:
new_df.groupby(['Year_of_Death']).sum()['Maternal_disorders'].sort_values(ascending = False)
Out[210]:
Year_of_Death
1990   3023030.0306
1992   3010784.7634
1991   2993140.3970
1994   2919339.6642
1995   2901961.0306
1997   2886994.6642
1996   2875882.0306
2000   2870677.6642
1998   2869753.9313
1999   2868103.9313
1993   2830872.0000
2001   2817243.2978
2002   2756833.2978
2003   2690640.2978
2005   2653197.6642
2004   2633311.5649
2006   2585375.9313
2007   2537652.6642
2008   2491210.6642
2009   2435245.9313
2010   2392537.9313
2011   2342250.9313
2012   2258980.5649
2013   2230906.9313
2014   2145690.9313
2015   2081779.9313
2016   2027219.5649
2017   1973958.1985
2018   1908483.0000
2019   1892483.0000
Name: Maternal_disorders, dtype: float64
In [211]:
fig = px.bar(new_df, x = 'Year_of_Death', y = 'Maternal_disorders', color = 'Maternal_disorders')
fig.show()
In [212]:
new_df.groupby(['Year_of_Death']).sum()['Lower_respiratory_infections'].sort_values(ascending = False)
Out[212]:
Year_of_Death
1990   33932816.7965
1991   33715996.8689
1992   33606797.9413
1994   32650901.7241
1995   32414420.7965
1993   31974430.0000
1996   31879885.7965
1997   31379054.7241
1998   30723395.5793
1999   30260562.5793
2000   29925864.7241
2001   29157664.6517
2002   28756224.6517
2003   28345485.6517
2005   27771849.7241
2004   27642279.5069
2006   27257734.5793
2007   27188599.7241
2008   27048864.7241
2009   26524081.5793
2010   26276707.5793
2013   26242641.5793
2015   26235854.5793
2014   26204173.5793
2011   26115335.5793
2012   26017300.5069
2016   25971009.5069
2017   25591427.4345
2019   25041269.0000
2018   24974134.0000
Name: Lower_respiratory_infections, dtype: float64
In [213]:
fig = px.bar(new_df, x = 'Year_of_Death', y = 'Lower_respiratory_infections', color = 'Lower_respiratory_infections')
fig.show()
In [214]:
new_df.groupby(['Year_of_Death']).sum()['Neonatal_disorders'].sort_values(ascending = False)
Out[214]:
Year_of_Death
1990   29949352.3528
1991   29761812.9303
1992   29617007.5079
1994   28829370.7753
1995   28706087.3528
1996   28400568.3528
1993   28164872.0000
1997   28090018.7753
1998   27687828.6202
1999   27436908.6202
2000   27369937.7753
2001   27001871.1978
2002   26827207.1978
2003   26636230.1978
2005   26254143.7753
2004   26231067.0427
2006   25748573.6202
2007   25576518.7753
2008   25257848.7753
2009   24681615.6202
2010   24238073.6202
2011   23806620.6202
2012   23301105.0427
2013   23008173.6202
2014   22413443.6202
2015   21898408.6202
2016   20994612.0427
2017   20083722.4652
2018   18833230.0000
2019   18245288.0000
Name: Neonatal_disorders, dtype: float64
In [215]:
fig = px.bar(new_df, x = 'Year_of_Death', y = 'Neonatal_disorders', color = 'Neonatal_disorders')
fig.show()
In [216]:
new_df.groupby(['Year_of_Death']).sum()[ 'Alcohol_use_disorders'].sort_values(ascending = False)
Out[216]:
Year_of_Death
2005   1900320.6117
2004   1869142.1282
2003   1865017.4506
2006   1849987.2894
2002   1828438.4506
2007   1825957.6117
2008   1803677.6117
2001   1782497.4506
2009   1744117.2894
2000   1740664.6117
2010   1721060.2894
2019   1709565.0000
2016   1696197.1282
2017   1695005.9670
2011   1692581.2894
2015   1688641.2894
2018   1685875.0000
2014   1674131.2894
1999   1673449.2894
2013   1668957.2894
2012   1667714.1282
1995   1651730.7729
1996   1648098.7729
1997   1633218.6117
1998   1630028.2894
1994   1617404.6117
1993   1456631.0000
1992   1414127.0953
1991   1316473.9341
1990   1249542.7729
Name: Alcohol_use_disorders, dtype: float64
In [217]:
fig = px.bar(new_df, x = 'Year_of_Death', y =  'Alcohol_use_disorders', color =  'Alcohol_use_disorders')
fig.show()
In [218]:
new_df.groupby(['Year_of_Death']).sum()['Exposure_to_forces_of_nature'].sort_values(ascending = False)
Out[218]:
Year_of_Death
2004   2445688.9285
2008   2228889.1835
2010   1987123.3468
1991   1436020.0202
2005    828567.1835
1999    642996.3468
1998    438573.3468
1990    437229.6019
2011    342918.3468
2003    311628.7652
2001    302833.7652
2013    265597.3468
1995    208433.6019
2006    204778.3468
1993    203190.0000
1997    181496.1835
2007    167128.1835
1996    162359.6019
2015    139888.3468
1994    137975.1835
1992    136285.4386
2017    129267.5101
2009    118694.3468
2018    109282.0000
2000    103354.1835
2002     98126.7652
2012     95662.9285
2016     94319.9285
2014     83803.3468
2019     59173.0000
Name: Exposure_to_forces_of_nature, dtype: float64
In [219]:
fig = px.bar(new_df, x = 'Year_of_Death', y = 'Exposure_to_forces_of_nature', color = 'Exposure_to_forces_of_nature')
fig.show()
In [220]:
new_df.groupby(['Year_of_Death']).sum()['Diarrheal_diseases']
Out[220]:
Year_of_Death
1990   29251537.1969
1991   29439506.0330
1992   29218873.8690
1993   27432388.0000
1994   27684815.3608
1995   27111815.1969
1996   26564254.1969
1997   26145626.3608
1998   25658341.6886
1999   25205339.6886
2000   24827374.3608
2001   24156640.5247
2002   23520204.5247
2003   22845752.5247
2004   21943699.8526
2005   21794590.3608
2006   21491562.6886
2007   21342752.3608
2008   21030841.3608
2009   20202987.6886
2010   19737087.6886
2011   19469816.6886
2012   18727441.8526
2013   18348864.6886
2014   17729036.6886
2015   17266065.6886
2016   16795571.8526
2017   16636431.0165
2018   15616728.0000
2019   15181793.0000
Name: Diarrheal_diseases, dtype: float64
In [221]:
fig = px.bar(new_df, x = 'Year_of_Death', y = 'Diarrheal_diseases', color = 'Diarrheal_diseases')
fig.show()
In [222]:
new_df.groupby(['Year_of_Death']).sum()['Environmental_heat_and_cold_exposure'].sort_values(ascending = False)
Out[222]:
Year_of_Death
1995   750927.6152
1994   745401.5593
2003   742497.5034
2002   719415.5034
2005   706402.5593
1996   700090.6152
2001   687579.5034
2004   687161.3915
2000   677036.5593
1993   668640.0000
1998   667525.4474
2006   665131.4474
1997   660825.5593
1999   652485.4474
1992   628736.7271
2007   620949.5593
2010   606374.4474
2008   600800.5593
1991   593810.6712
1990   576158.6152
2009   567654.4474
2011   535962.4474
2015   535081.4474
2012   531958.3915
2013   515092.4474
2014   506762.4474
2016   492241.3915
2019   479449.0000
2017   477866.3356
2018   464640.0000
Name: Environmental_heat_and_cold_exposure, dtype: float64
In [223]:
fig = px.bar(new_df, x = 'Year_of_Death', y = 'Environmental_heat_and_cold_exposure', color = 'Environmental_heat_and_cold_exposure')
fig.show()
In [224]:
new_df.groupby(['Year_of_Death']).sum()['Nutritional_deficiencies'].sort_values(ascending = False)
Out[224]:
Year_of_Death
1990   7482044.6303
1991   7237207.6876
1995   7098006.6303
1992   6978513.7449
1996   6619370.6303
1993   6507485.0000
1994   6437941.5730
1997   6368868.5730
1998   6071071.4584
1999   5807843.4584
2000   5599694.5730
2001   5333006.5157
2002   5094431.5157
2003   4173176.5157
2004   3916311.4011
2005   3799403.5730
2006   3632662.4584
2007   3523016.5730
2008   3409643.5730
2009   3255591.4584
2010   3246251.4584
2011   3173359.4584
2012   3010665.4011
2013   2964201.4584
2014   2897287.4584
2015   2843148.4584
2016   2771400.4011
2017   2697389.3438
2018   2508095.0000
2019   2444227.0000
Name: Nutritional_deficiencies, dtype: float64
In [225]:
fig = px.bar(new_df, x = 'Year_of_Death', y = 'Nutritional_deficiencies', color = 'Nutritional_deficiencies')
fig.show()
In [226]:
new_df.groupby(['Year_of_Death']).sum()['Self-harm'].sort_values(ascending = False)
Out[226]:
Year_of_Death
2000   8821783.2447
1999   8781565.1958
1995   8726805.7692
2005   8683788.2447
1998   8674909.1958
1996   8664226.7692
1997   8661926.2447
1994   8618473.2447
2001   8604733.7202
2003   8542524.7202
2004   8525594.6713
2002   8508446.7202
2006   8503527.1958
2007   8447368.2447
2008   8399150.2447
1992   8245210.8181
2009   8223743.1958
2010   8207513.1958
2011   8132530.1958
1993   8064378.0000
1991   8042053.2936
2012   7997491.6713
2013   7909681.1958
1990   7875843.7692
2014   7798169.1958
2015   7771952.1958
2016   7740495.6713
2017   7714167.1468
2019   7682558.0000
2018   7648006.0000
Name: Self-harm, dtype: float64
In [227]:
fig = px.bar(new_df, x = 'Year_of_Death', y = 'Self-harm', color = 'Self-harm')
fig.show()
In [228]:
new_df.groupby(['Year_of_Death']).sum()['Conflict_and_terrorism'].sort_values(ascending = False)
Out[228]:
Year_of_Death
1994   6143653.5443
2014   1626829.2355
2015   1449403.2355
2016   1370595.5810
1999   1334037.2355
2000   1135915.5443
2017   1118331.9266
1990   1110766.1988
1998   1030349.2355
2013   1023420.2355
2012    963698.5810
1997    953352.5443
2018    876568.0000
1996    847653.1988
1991    838714.8532
2009    682413.2355
1995    676303.1988
2003    662922.8899
2006    657245.2355
2001    653464.8899
2004    645512.5810
2007    642089.5443
1992    628510.5076
2002    627076.8899
2008    613769.5443
2011    572363.2355
1993    572100.0000
2005    549036.5443
2019    529821.0000
2010    529285.2355
Name: Conflict_and_terrorism, dtype: float64
In [229]:
fig = px.bar(new_df, x = 'Year_of_Death', y = 'Conflict_and_terrorism', color = 'Conflict_and_terrorism')
fig.show()
In [230]:
new_df.groupby(['Year_of_Death']).sum()['Diabetes_mellitus'].sort_values(ascending = False)
Out[230]:
Year_of_Death
2019   15409833.0000
2018   14939751.0000
2017   14717712.3670
2016   14348660.5949
2015   13936608.8227
2014   13488725.8227
2013   13072960.8227
2012   12641478.5949
2011   12305027.8227
2010   11993099.8227
2009   11772535.8227
2008   11662758.2784
2007   11376903.2784
2006   11088470.8227
2005   10974566.2784
2004   10534450.5949
2003   10448688.0506
2002   10153706.0506
2001    9798067.0506
2000    9550049.2784
1999    9208636.8227
1998    8957463.8227
1997    8770191.2784
1996    8518085.5062
1995    8263912.5062
1994    8005891.2784
1992    7643305.9619
1993    7384169.0000
1991    7382669.7341
1990    7158244.5062
Name: Diabetes_mellitus, dtype: float64
In [231]:
fig = px.bar(new_df, x = 'Year_of_Death', y = 'Diabetes_mellitus', color = 'Diabetes_mellitus')
fig.show()
In [232]:
new_df.groupby(['Year_of_Death']).sum()['Poisonings'].sort_values(ascending = False)
Out[232]:
Year_of_Death
2005   936083.1111
1994   923220.1111
1995   920443.2222
2004   920138.7778
2003   918305.0000
2002   910720.0000
1992   909005.4444
2006   908342.8889
1996   905442.2222
2001   904217.0000
2008   903574.1111
2000   902435.1111
2007   901256.1111
1991   900314.3333
1990   898864.2222
2009   889897.8889
2010   887901.8889
1997   887887.1111
1993   882970.0000
1999   881250.8889
1998   874906.8889
2011   872625.8889
2012   854664.7778
2013   848172.8889
2014   832759.8889
2015   822652.8889
2016   809336.7778
2017   791345.6667
2018   765508.0000
2019   758680.0000
Name: Poisonings, dtype: float64
In [233]:
fig = px.bar(new_df, x = 'Year_of_Death', y = 'Poisonings', color = 'Poisonings')
fig.show()
In [234]:
new_df.groupby(['Year_of_Death']).sum()['Protein-energy_malnutrition'].sort_values(ascending = False)
Out[234]:
Year_of_Death
1990   6471534.2297
1995   6271585.2297
1991   6249856.6142
1992   6022943.9988
1996   5830991.2297
1993   5622720.0000
1997   5610297.8452
1994   5576329.8452
1998   5359881.0762
1999   5145720.0762
2000   4967192.8452
2001   4733175.4607
2002   4532484.4607
2003   3651981.4607
2004   3430215.6916
2005   3321914.8452
2006   3165333.0762
2007   3060478.8452
2008   2951051.8452
2009   2814886.0762
2010   2810585.0762
2011   2739772.0762
2012   2581215.6916
2013   2538171.0762
2014   2475561.0762
2015   2426802.0762
2016   2361481.6916
2017   2292121.3071
2018   2120805.0000
2019   2062098.0000
Name: Protein-energy_malnutrition, dtype: float64
In [235]:
fig = px.bar(new_df, x = 'Year_of_Death', y = 'Protein-energy_malnutrition', color = 'Protein-energy_malnutrition')
fig.show()
In [236]:
new_df.groupby(['Year_of_Death']).sum()['Terrorism'].sort_values(ascending = False)
Out[236]:
Year_of_Death
2014   190046.2165
2015   173484.4524
2016   158046.0934
2017   134514.2729
2013   125141.3961
2012   108305.7551
2007   103429.8782
2006    94844.0578
2019    93245.9865
2018    93245.9865
1993    93245.9865
2009    90332.2269
2011    89695.8782
2004    89520.8322
2010    89137.3501
2005    87792.4732
2008    87539.5756
1990    84655.6987
1997    83411.2062
2001    81160.1602
2002    79722.1141
1998    79720.3501
1991    76624.6780
2003    75258.1141
1992    73229.7240
2000    71182.1602
1999    70596.8115
1996    70489.4984
1994    67772.1958
1995    67202.0266
Name: Terrorism, dtype: float64
In [237]:
fig = px.bar(new_df, x = 'Year_of_Death', y = 'Terrorism', color = 'Terrorism')
fig.show()
In [238]:
new_df.groupby(['Year_of_Death']).sum()['Cardiovascular_diseases'].sort_values(ascending = False)
Out[238]:
Year_of_Death
2019   186591929.0000
2018   182185452.0000
2017   181262400.4337
2016   178986724.1727
2015   176529080.9116
2014   172833776.9116
2013   170640947.9116
2012   167696295.1727
2011   166208710.9116
2010   164238697.9116
2009   161477181.9116
2008   161211489.3895
2007   158482656.3895
2005   157518557.3895
2006   156208079.9116
2003   154531129.6506
2004   153470631.1727
2002   152591536.6506
2001   149569869.6506
2000   148011255.3895
1999   144719830.9116
1997   142261492.3895
1998   142255338.9116
1996   141650682.1285
1995   140908200.1285
1994   139395446.3895
1992   135487138.6064
1991   132898527.8674
1993   131636930.0000
1990   130850467.1285
Name: Cardiovascular_diseases, dtype: float64
In [239]:
fig = px.bar(new_df, x = 'Year_of_Death', y = 'Malaria', color = 'Cardiovascular_diseases')
fig.show()
In [240]:
new_df.groupby(['Year_of_Death']).sum()['Chronic_kidney_disease'].sort_values(ascending = False)
Out[240]:
Year_of_Death
2019   14260228.0000
2018   13878564.0000
2017   13736980.7176
2016   13518940.1705
2015   13183089.6235
2014   12785215.6235
2013   12455570.6235
2012   12041648.1705
2011   11743365.6235
2010   11382887.6235
2009   11046540.6235
2008   10839064.5293
2007   10484535.5293
2006   10123539.6235
2005    9941601.5293
2004    9495341.1705
2003    9364362.0764
2002    9083340.0764
2001    8769196.0764
2000    8524580.5293
1999    8154976.6235
1998    7885471.6235
1997    7679627.5293
1996    7448281.9823
1995    7239234.9823
1994    7019020.5293
1992    6778985.8881
1991    6581273.4352
1993    6477967.0000
1990    6421136.9823
Name: Chronic_kidney_disease, dtype: float64
In [241]:
fig = px.bar(new_df, x = 'Year_of_Death', y = 'Chronic_kidney_disease', color = 'Chronic_kidney_disease')
fig.show()
In [242]:
new_df.groupby(['Year_of_Death']).sum()['Chronic_Respiratory_diseases'].sort_values(ascending = False)
Out[242]:
Year_of_Death
2019   40016895.0000
2018   39151687.0000
2017   39024487.4951
2016   38610803.7443
2015   38250571.9935
2014   37843083.9935
2013   37439201.9935
2012   36778059.7443
2011   36703234.9935
2008   36662561.4919
2010   36398710.9935
2003   36344655.2427
2005   36341575.4919
2002   36324463.2427
2009   36278019.9935
2007   36228137.4919
2001   36073391.2427
2000   36047488.4919
2006   35898880.9935
2004   35778628.7443
1999   35486341.9935
1997   35352664.4919
1998   35294173.9935
1996   35080883.7411
1995   34782877.7411
1994   34487236.4919
1992   33996163.2395
1991   33276176.9903
1993   32872069.0000
1990   32588183.7411
Name: Chronic_Respiratory_diseases, dtype: float64
In [243]:
fig = px.bar(new_df, x = 'Year_of_Death', y = 'Chronic_Respiratory_diseases', color = 'Chronic_Respiratory_diseases')
fig.show()
In [244]:
new_df.groupby(['Year_of_Death']).sum()['Cirrhosis_liver_diseases'].sort_values(ascending = False)
Out[244]:
Year_of_Death
2019   14591030.0000
2017   14432221.0112
2018   14362805.0000
2016   14268548.3464
2015   14132792.6816
2014   13926965.6816
2013   13892259.6816
2012   13805015.3464
2011   13804194.6816
2008   13732511.3521
2010   13729498.6816
2009   13633165.6816
2007   13552004.3521
2005   13380611.3521
2006   13346319.6816
2004   12970987.3464
2003   12928752.0169
2002   12692165.0169
2001   12423727.0169
2000   12249036.3521
1999   11971720.6816
1998   11804089.6816
1997   11773243.3521
1996   11681764.6873
1995   11576661.6873
1994   11382063.3521
1992   11066883.3577
1991   10862762.0225
1993   10703659.0000
1990   10671551.6873
Name: Cirrhosis_liver_diseases, dtype: float64
In [245]:
fig = px.bar(new_df, x = 'Year_of_Death', y = 'Cirrhosis_liver_diseases', color = 'Cirrhosis_liver_diseases')
fig.show()
In [246]:
new_df.groupby(['Year_of_Death']).sum()['Digestive_diseases'].sort_values(ascending = False)
Out[246]:
Year_of_Death
2019   25696297.0000
2017   25411151.4577
2018   25288011.0000
2016   25117581.3673
2015   24857941.2769
2014   24459532.2769
2013   24316539.2769
2012   24044622.3673
2011   23998490.2769
2010   23819227.2769
2008   23811656.0961
2009   23638516.2769
2007   23527698.0961
2005   23276918.0961
2006   23202617.2769
2003   22684489.1865
2004   22647249.3673
2002   22386264.1865
2001   22021787.1865
2000   21821619.0961
1999   21405667.2769
1998   21164811.2769
1997   21164543.0961
1996   21047852.0057
1995   20934902.0057
1994   20684270.0961
1992   20297518.8250
1991   19956234.9154
1990   19640078.0057
1993   19579378.0000
Name: Digestive_diseases, dtype: float64
In [247]:
fig = px.bar(new_df, x = 'Year_of_Death', y = 'Digestive_diseases', color = 'Digestive_diseases')
fig.show()
In [248]:
new_df.groupby(['Year_of_Death']).sum()['Acute_hepatitis'].sort_values(ascending = False)
Out[248]:
Year_of_Death
1990   1661965.4925
1991   1657379.7191
1992   1646588.9457
1994   1591217.2659
1995   1574135.4925
1993   1569312.0000
1996   1537720.4925
1997   1519494.2659
1998   1488657.8127
1999   1464685.8127
2000   1446091.2659
2001   1403053.0393
2002   1352192.0393
2003   1305056.0393
2005   1291444.2659
2004   1274594.5861
2006   1248981.8127
2007   1217801.2659
2008   1186935.2659
2009   1132842.8127
2010   1103008.8127
2011   1073791.8127
2012   1050202.5861
2013    998151.8127
2014    937672.8127
2015    896927.8127
2016    852222.5861
2017    825085.3596
2018    782313.0000
2019    765188.0000
Name: Acute_hepatitis, dtype: float64
In [249]:
fig = px.bar(new_df, x = 'Year_of_Death', y = 'Acute_hepatitis', color = 'Acute_hepatitis')
fig.show()
In [250]:
new_df.groupby(['Year_of_Death']).sum()['Alzheimer disease'].sort_values(ascending = False)
Out[250]:
Year_of_Death
2019   16988702.0000
2018   16437635.0000
2017   16067495.6794
2016   15515803.6260
2015   14972793.5725
2014   14441797.5725
2013   13938472.5725
2012   13414069.6260
2011   12971799.5725
2010   12499823.5725
2009   12014697.5725
2008   11637073.4657
2007   11188383.4657
2006   10696557.5725
2005   10393292.4657
2004    9894720.6260
2003    9641247.5191
2002    9314888.5191
2001    8989577.5191
2000    8737762.4657
1999    8399477.5725
1998    8161537.5725
1997    8017137.4657
1996    7840580.4122
1995    7610644.4122
1994    7330315.4657
1992    6955305.3054
1993    6697687.0000
1991    6673846.3588
1990    6393869.4122
Name: Alzheimer disease, dtype: float64
In [251]:
fig = px.bar(new_df, x = 'Year_of_Death', y = 'Alzheimer disease', color = 'Alzheimer disease')
fig.show()
In [252]:
new_df.groupby(['Year_of_Death']).sum()['Parkinson disease'].sort_values(ascending = False)
Out[252]:
Year_of_Death
2019   3770569.0000
2018   3654087.0000
2017   3586867.1019
2016   3509369.1189
2015   3429082.1358
2014   3336769.1358
2013   3240934.1358
2012   3126003.1189
2011   3045024.1358
2010   2958818.1358
2009   2872161.1358
2008   2815241.1698
2007   2712474.1698
2006   2612923.1358
2005   2566524.1698
2004   2447128.1189
2003   2410932.1528
2002   2334243.1528
2001   2243045.1528
2000   2172943.1698
1999   2082333.1358
1998   2015003.1358
1997   1973769.1698
1996   1925851.1868
1995   1874415.1868
1994   1817816.1698
1992   1748998.2207
1991   1698855.2037
1993   1682132.0000
1990   1651046.1868
Name: Parkinson disease, dtype: float64
In [253]:
fig = px.bar(new_df, x = 'Year_of_Death', y = 'Parkinson disease', color = 'Parkinson disease')
fig.show()
In [ ]:
In [258]:
new_df.columns
Out[258]:
Index(['Country', 'Year_of_Death', 'Meningitis', 'Neoplasms',
       'Fire_heat_and_hot_substances', 'Malaria', 'Drowning',
       'Interpersonal_violence', 'HIV/AIDS', 'Drug_use_disorders',
       'Tuberculosis', 'Road_injuries', 'Maternal_disorders',
       'Lower_respiratory_infections', 'Neonatal_disorders',
       'Alcohol_use_disorders', 'Exposure_to_forces_of_nature',
       'Diarrheal_diseases', 'Environmental_heat_and_cold_exposure',
       'Nutritional_deficiencies', 'Self-harm', 'Conflict_and_terrorism',
       'Diabetes_mellitus', 'Poisonings', 'Protein-energy_malnutrition',
       'Terrorism', 'Cardiovascular_diseases', 'Chronic_kidney_disease',
       'Chronic_Respiratory_diseases', 'Cirrhosis_liver_diseases',
       'Digestive_diseases', 'Acute_hepatitis', 'Alzheimer disease',
       'Parkinson disease'],
      dtype='object')
In [300]:
new_df.groupby(['Country']).sum()['Meningitis'].sort_values(ascending = False)[:20]
Out[300]:
Country
World                                    10530812.0000
World Bank Lower Middle Income            6015299.0000
Commonwealth                              5676380.0000
African Union                             5559509.0000
Africa                                    5559509.0000
Sub-Saharan Africa - World Bank region    5466427.0000
African Region                            5325372.0000
Low SDI                                   4907585.0000
Commonwealth Middle Income                4749263.0000
Asia                                      4380260.0000
G20                                       3334483.0000
Low-middle SDI                            3315974.0000
World Bank Low Income                     3262367.0000
Western sub-Saharan Africa                3036082.0000
South Asia - World Bank region            2985858.0000
South-East Asia Region                    2684670.0000
India                                     2008944.0000
Eastern sub-Saharan Africa                1787598.0000
Middle SDI                                1667936.0000
Nigeria                                   1520376.0000
Name: Meningitis, dtype: float64
In [303]:
fig = px.bar(new_df, x = 'Country', y = 'Meningitis', color = 'Meningitis')
fig.show()
In [306]:
Out[306]:
array(['Afghanistan', 'Africa', 'African Region', 'African Union',
       'Albania', 'Algeria', 'America', 'American Samoa',
       'Andean Latin America', 'Andorra', 'Angola', 'Antigua and Barbuda',
       'Argentina', 'Armenia', 'Asia', 'Australasia',
       'Australasia & Oceania', 'Australia', 'Austria', 'Azerbaijan',
       'Bahamas', 'Bahrain', 'Bangladesh', 'Barbados', 'Belarus',
       'Belgium', 'Belize', 'Benin', 'Bermuda', 'Bhutan', 'Bolivia',
       'Bosnia and Herzegovina', 'Bosnia-Herzegovina', 'Botswana',
       'Brazil', 'Brunei', 'Bulgaria', 'Burkina Faso', 'Burundi',
       'Cambodia', 'Cameroon', 'Canada', 'Cape Verde', 'Caribbean',
       'Central African Republic', 'Central America & Caribbean',
       'Central Asia', 'Central Europe',
       'Central Europe, Eastern Europe, and Central Asia',
       'Central Latin America', 'Central sub-Saharan Africa', 'Chad',
       'Chile', 'China', 'Colombia', 'Commonwealth',
       'Commonwealth High Income', 'Commonwealth Low Income',
       'Commonwealth Middle Income', 'Comoros', 'Congo', 'Cook Islands',
       'Costa Rica', "Cote d'Ivoire", 'Croatia', 'Cuba', 'Cyprus',
       'Czechia', 'Czechoslovakia', 'Democratic Republic of Congo',
       'Denmark', 'Djibouti', 'Dominica', 'Dominican Republic',
       'East Asia', 'East Asia & Pacific - World Bank region',
       'East Germany (GDR)', 'East Timor', 'Eastern Europe',
       'Eastern Mediterranean Region', 'Eastern sub-Saharan Africa',
       'Ecuador', 'Egypt', 'El Salvador', 'England', 'Equatorial Guinea',
       'Eritrea', 'Estonia', 'Eswatini', 'Ethiopia', 'Europe',
       'Europe & Central Asia - World Bank region', 'European Region',
       'European Union', 'Fiji', 'Finland', 'France', 'French Guiana',
       'French Polynesia', 'G20', 'Gabon', 'Gambia', 'Georgia', 'Germany',
       'Ghana', 'Greece', 'Greenland', 'Grenada', 'Guadeloupe', 'Guam',
       'Guatemala', 'Guinea', 'Guinea-Bissau', 'Guyana', 'Haiti',
       'High SDI', 'High-income', 'High-income Asia Pacific',
       'High-income North America', 'High-middle SDI', 'Honduras',
       'Hong Kong', 'Hungary', 'Iceland', 'India', 'Indonesia',
       'International', 'Iran', 'Iraq', 'Ireland', 'Israel', 'Italy',
       'Jamaica', 'Japan', 'Jordan', 'Kazakhstan', 'Kenya', 'Kiribati',
       'Kosovo', 'Kuwait', 'Kyrgyzstan', 'Laos',
       'Latin America & Caribbean - World Bank region', 'Latvia',
       'Lebanon', 'Lesotho', 'Liberia', 'Libya', 'Lithuania', 'Low SDI',
       'Low-middle SDI', 'Luxembourg', 'Macau', 'Madagascar', 'Malawi',
       'Malaysia', 'Maldives', 'Mali', 'Malta', 'Marshall Islands',
       'Martinique', 'Mauritania', 'Mauritius', 'Mexico',
       'Micronesia (country)', 'Middle East & North Africa', 'Middle SDI',
       'Moldova', 'Monaco', 'Mongolia', 'Montenegro', 'Morocco',
       'Mozambique', 'Myanmar', 'Namibia', 'Nauru', 'Nepal',
       'Netherlands', 'New Caledonia', 'New Zealand', 'Nicaragua',
       'Niger', 'Nigeria', 'Niue', 'Nordic Region',
       'North Africa and Middle East', 'North America', 'North Korea',
       'North Macedonia', 'Northern Ireland', 'Northern Mariana Islands',
       'Norway', 'OECD Countries', 'Oceania', 'Oman', 'Pakistan', 'Palau',
       'Palestine', 'Panama', 'Papua New Guinea', 'Paraguay', 'Peru',
       'Philippines', 'Poland', 'Portugal', 'Puerto Rico', 'Qatar',
       'Region of the Americas', 'Romania', 'Russia', 'Rwanda',
       'Saint Kitts and Nevis', 'Saint Lucia',
       'Saint Vincent and the Grenadines', 'Samoa', 'San Marino',
       'Sao Tome and Principe', 'Saudi Arabia', 'Scotland', 'Senegal',
       'Serbia', 'Serbia-Montenegro', 'Seychelles', 'Sierra Leone',
       'Singapore', 'Slovakia', 'Slovenia', 'Solomon Islands', 'Somalia',
       'South Africa', 'South America', 'South Asia',
       'South Asia - World Bank region', 'South Korea', 'South Sudan',
       'South-East Asia Region', 'Southeast Asia',
       'Southeast Asia, East Asia, and Oceania', 'Southern Latin America',
       'Southern sub-Saharan Africa', 'Spain', 'Sri Lanka',
       'Sub-Saharan Africa', 'Sub-Saharan Africa - World Bank region',
       'Sudan', 'Suriname', 'Sweden', 'Switzerland', 'Syria', 'Taiwan',
       'Tajikistan', 'Tanzania', 'Thailand', 'Timor', 'Togo', 'Tokelau',
       'Tonga', 'Trinidad and Tobago', 'Tropical Latin America',
       'Tunisia', 'Turkey', 'Turkmenistan', 'Tuvalu', 'USSR', 'Uganda',
       'Ukraine', 'United Arab Emirates', 'United Kingdom',
       'United States', 'United States Virgin Islands', 'Uruguay',
       'Uzbekistan', 'Vanuatu', 'Venezuela', 'Vietnam', 'Wales',
       'Wallis and Futuna', 'West Germany (FRG)', 'Western Europe',
       'Western Pacific Region', 'Western Sahara',
       'Western sub-Saharan Africa', 'World', 'World (excluding China)',
       'World Bank High Income', 'World Bank Low Income',
       'World Bank Lower Middle Income', 'World Bank Upper Middle Income',
       'Yemen', 'Yugoslavia', 'Zaire', 'Zambia', 'Zimbabwe'], dtype=object)
In [307]:
new_df.columns
Out[307]:
Index(['Country', 'Year_of_Death', 'Meningitis', 'Neoplasms',
       'Fire_heat_and_hot_substances', 'Malaria', 'Drowning',
       'Interpersonal_violence', 'HIV/AIDS', 'Drug_use_disorders',
       'Tuberculosis', 'Road_injuries', 'Maternal_disorders',
       'Lower_respiratory_infections', 'Neonatal_disorders',
       'Alcohol_use_disorders', 'Exposure_to_forces_of_nature',
       'Diarrheal_diseases', 'Environmental_heat_and_cold_exposure',
       'Nutritional_deficiencies', 'Self-harm', 'Conflict_and_terrorism',
       'Diabetes_mellitus', 'Poisonings', 'Protein-energy_malnutrition',
       'Terrorism', 'Cardiovascular_diseases', 'Chronic_kidney_disease',
       'Chronic_Respiratory_diseases', 'Cirrhosis_liver_diseases',
       'Digestive_diseases', 'Acute_hepatitis', 'Alzheimer disease',
       'Parkinson disease'],
      dtype='object')
In [ ]:
In [371]:
new_df.pivot_table(index = ['Country', 'Year_of_Death'], values = ['Meningitis', 'Neoplasms',
       'Fire_heat_and_hot_substances', 'Malaria', 'Drowning',
       'Interpersonal_violence', 'HIV/AIDS', 'Drug_use_disorders',
       'Tuberculosis', 'Road_injuries', 'Maternal_disorders',
       'Lower_respiratory_infections', 'Neonatal_disorders',
       'Alcohol_use_disorders', 'Exposure_to_forces_of_nature',
       'Diarrheal_diseases', 'Environmental_heat_and_cold_exposure',
       'Nutritional_deficiencies', 'Self-harm', 'Conflict_and_terrorism',
       'Diabetes_mellitus', 'Poisonings', 'Protein-energy_malnutrition',
       'Terrorism', 'Cardiovascular_diseases', 'Chronic_kidney_disease',
       'Chronic_Respiratory_diseases', 'Cirrhosis_liver_diseases',
       'Digestive_diseases', 'Acute_hepatitis', 'Alzheimer disease',
       'Parkinson disease'])
Out[371]:
Acute_hepatitis Alcohol_use_disorders Alzheimer disease Cardiovascular_diseases Chronic_Respiratory_diseases Chronic_kidney_disease Cirrhosis_liver_diseases Conflict_and_terrorism Diabetes_mellitus Diarrheal_diseases Digestive_diseases Drowning Drug_use_disorders Environmental_heat_and_cold_exposure Exposure_to_forces_of_nature Fire_heat_and_hot_substances HIV/AIDS Interpersonal_violence Lower_respiratory_infections Malaria Maternal_disorders Meningitis Neonatal_disorders Neoplasms Nutritional_deficiencies Parkinson disease Poisonings Protein-energy_malnutrition Road_injuries Self-harm Terrorism Tuberculosis
Country Year_of_Death
Afghanistan 1990 2985.0000 72.0000 1116.0000 44899.0000 5945.0000 3709.0000 2673.0000 1490.0000 2108.0000 4235.0000 5005.0000 1370.0000 93.0000 175.0000 0.0000 323.0000 34.0000 1538.0000 23741.0000 93.0000 2655.0000 2159.0000 15612.0000 11580.0000 2087.0000 371.0000 338.0000 2054.0000 4154.0000 696.0000 12.0000 4661.0000
1991 3092.0000 75.0000 1136.0000 45492.0000 6050.0000 3724.0000 2728.0000 3370.0000 2120.0000 4927.0000 5120.0000 1391.0000 102.0000 113.0000 1347.0000 332.0000 41.0000 2001.0000 24504.0000 189.0000 2885.0000 2218.0000 17128.0000 11796.0000 2153.0000 374.0000 351.0000 2119.0000 4472.0000 751.0000 68.0000 4743.0000
1992 3325.0000 80.0000 1162.0000 46557.0000 6223.0000 3776.0000 2830.0000 4344.0000 2153.0000 6123.0000 5335.0000 1514.0000 118.0000 38.0000 614.0000 360.0000 48.0000 2299.0000 27404.0000 239.0000 3315.0000 2475.0000 20060.0000 12218.0000 2441.0000 378.0000 386.0000 2404.0000 5106.0000 855.0000 49.0000 4976.0000
1993 3601.0000 85.0000 1187.0000 47951.0000 6445.0000 3862.0000 2943.0000 4096.0000 2195.0000 8174.0000 5568.0000 1687.0000 132.0000 41.0000 225.0000 396.0000 56.0000 2589.0000 31116.0000 108.0000 3671.0000 2812.0000 22335.0000 12634.0000 2837.0000 384.0000 425.0000 2797.0000 5681.0000 943.0000 349.2359 5254.0000
1994 3816.0000 88.0000 1211.0000 49308.0000 6664.0000 3932.0000 3027.0000 8959.0000 2231.0000 8215.0000 5739.0000 1809.0000 142.0000 44.0000 160.0000 420.0000 63.0000 2849.0000 33390.0000 211.0000 3863.0000 3027.0000 23288.0000 12914.0000 3081.0000 391.0000 451.0000 3038.0000 6001.0000 993.0000 22.0000 5470.0000
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
Zimbabwe 2015 146.0000 48.0000 754.0000 16649.0000 2751.0000 2108.0000 1956.0000 13.0000 3176.0000 5102.0000 4202.0000 770.0000 104.0000 37.0000 15.0000 632.0000 29162.0000 1302.0000 12974.0000 2518.0000 1355.0000 1439.0000 9278.0000 11161.0000 3019.0000 215.0000 381.0000 2990.0000 2373.0000 2235.0000 349.2359 11214.0000
2016 146.0000 49.0000 767.0000 16937.0000 2788.0000 2160.0000 1962.0000 6.0000 3259.0000 5002.0000 4264.0000 801.0000 110.0000 37.0000 31.0000 648.0000 27141.0000 1342.0000 13024.0000 2050.0000 1338.0000 1457.0000 9065.0000 11465.0000 3056.0000 219.0000 393.0000 3027.0000 2436.0000 2297.0000 349.2359 10998.0000
2017 144.0000 50.0000 781.0000 17187.0000 2818.0000 2196.0000 2007.0000 5.0000 3313.0000 4948.0000 4342.0000 818.0000 115.0000 37.0000 251.0000 654.0000 24846.0000 1363.0000 12961.0000 2116.0000 1312.0000 1460.0000 8901.0000 11744.0000 2990.0000 223.0000 398.0000 2962.0000 2473.0000 2338.0000 0.0000 10762.0000
2018 139.0000 51.0000 795.0000 17460.0000 2849.0000 2240.0000 2030.0000 9.0000 3381.0000 4745.0000 4377.0000 825.0000 121.0000 37.0000 0.0000 657.0000 22106.0000 1396.0000 12860.0000 2088.0000 1294.0000 1450.0000 8697.0000 12038.0000 2918.0000 227.0000 400.0000 2890.0000 2509.0000 2372.0000 349.2359 10545.0000
2019 136.0000 53.0000 812.0000 17810.0000 2891.0000 2292.0000 2065.0000 11.0000 3460.0000 4635.0000 4437.0000 827.0000 127.0000 37.0000 660.0000 662.0000 20722.0000 1434.0000 12897.0000 2068.0000 1294.0000 1450.0000 8609.0000 12353.0000 2884.0000 232.0000 405.0000 2855.0000 2554.0000 2403.0000 349.2359 10465.0000

8254 rows × 32 columns

In [368]:
new_df.pivot_table(index = ['Country'], values = ['Meningitis', 'Neoplasms',
       'Fire_heat_and_hot_substances', 'Malaria', 'Drowning',
       'Interpersonal_violence', 'HIV/AIDS', 'Drug_use_disorders',
       'Tuberculosis', 'Road_injuries', 'Maternal_disorders',
       'Lower_respiratory_infections', 'Neonatal_disorders',
       'Alcohol_use_disorders', 'Exposure_to_forces_of_nature',
       'Diarrheal_diseases', 'Environmental_heat_and_cold_exposure',
       'Nutritional_deficiencies', 'Self-harm', 'Conflict_and_terrorism',
       'Diabetes_mellitus', 'Poisonings', 'Protein-energy_malnutrition',
       'Terrorism', 'Cardiovascular_diseases', 'Chronic_kidney_disease',
       'Chronic_Respiratory_diseases', 'Cirrhosis_liver_diseases',
       'Digestive_diseases', 'Acute_hepatitis', 'Alzheimer disease',
       'Parkinson disease']).sum().sort_values(ascending = False)
Out[368]:
Cardiovascular_diseases                166212377.5127
Neoplasms                               87430763.2434
Chronic_Respiratory_diseases            38529866.0122
Lower_respiratory_infections            30557577.2160
Neonatal_disorders                      27157753.2157
Digestive_diseases                      24206168.5166
Diarrheal_diseases                      23868024.9714
Tuberculosis                            16424194.1576
HIV/AIDS                                13844668.4669
Cirrhosis_liver_diseases                13679096.2154
Road_injuries                           13043367.9700
Diabetes_mellitus                       11554814.7572
Alzheimer disease                       11495546.3441
Chronic_kidney_disease                  10590617.7096
Malaria                                  9320929.0588
Self-harm                                8811249.6695
Nutritional_deficiencies                 4859714.7899
Interpersonal_violence                   4487543.5562
Protein-energy_malnutrition              4231325.6642
Meningitis                               3782542.4292
Drowning                                 3672062.9201
Parkinson disease                        2744535.9748
Maternal_disorders                       2729988.3602
Alcohol_use_disorders                    1789105.2238
Acute_hepatitis                          1343764.3914
Fire_heat_and_hot_substances             1302337.5567
Conflict_and_terrorism                   1102750.7486
Drug_use_disorders                       1016697.9654
Poisonings                                934409.5556
Environmental_heat_and_cold_exposure      655457.3875
Exposure_to_forces_of_nature              500566.5772
Terrorism                                  97073.9132
dtype: float64
In [370]:
new_df.pivot_table(index = ['Country'], values = ['Meningitis', 'Neoplasms',
       'Fire_heat_and_hot_substances', 'Malaria', 'Drowning',
       'Interpersonal_violence', 'HIV/AIDS', 'Drug_use_disorders',
       'Tuberculosis', 'Road_injuries', 'Maternal_disorders',
       'Lower_respiratory_infections', 'Neonatal_disorders',
       'Alcohol_use_disorders', 'Exposure_to_forces_of_nature',
       'Diarrheal_diseases', 'Environmental_heat_and_cold_exposure',
       'Nutritional_deficiencies', 'Self-harm', 'Conflict_and_terrorism',
       'Diabetes_mellitus', 'Poisonings', 'Protein-energy_malnutrition',
       'Terrorism', 'Cardiovascular_diseases', 'Chronic_kidney_disease',
       'Chronic_Respiratory_diseases', 'Cirrhosis_liver_diseases',
       'Digestive_diseases', 'Acute_hepatitis', 'Alzheimer disease',
       'Parkinson disease']).mean().sort_values(ascending = False)
Out[370]:
Cardiovascular_diseases                567277.7390
Neoplasms                              298398.5094
Chronic_Respiratory_diseases           131501.2492
Lower_respiratory_infections           104292.0724
Neonatal_disorders                      92688.5775
Digestive_diseases                      82614.9096
Diarrheal_diseases                      81460.8361
Tuberculosis                            56055.2702
HIV/AIDS                                47251.4282
Cirrhosis_liver_diseases                46686.3352
Road_injuries                           44516.6142
Diabetes_mellitus                       39436.2278
Alzheimer disease                       39233.9466
Chronic_kidney_disease                  36145.4529
Malaria                                 31812.0446
Self-harm                               30072.5245
Nutritional_deficiencies                16586.0573
Interpersonal_violence                  15315.8483
Protein-energy_malnutrition             14441.3845
Meningitis                              12909.7011
Drowning                                12532.6380
Parkinson disease                        9367.0170
Maternal_disorders                       9317.3664
Alcohol_use_disorders                    6106.1612
Acute_hepatitis                          4586.2266
Fire_heat_and_hot_substances             4444.8381
Conflict_and_terrorism                   3763.6544
Drug_use_disorders                       3469.9589
Poisonings                               3189.1111
Environmental_heat_and_cold_exposure     2237.0559
Exposure_to_forces_of_nature             1708.4184
Terrorism                                 331.3103
dtype: float64
In [461]:
new_df.pivot_table(index = 'Country', values = ['Meningitis', 'Neoplasms',
       'Fire_heat_and_hot_substances', 'Malaria', 'Drowning',
       'Interpersonal_violence', 'HIV/AIDS', 'Drug_use_disorders',
       'Tuberculosis', 'Road_injuries', 'Maternal_disorders',
       'Lower_respiratory_infections', 'Neonatal_disorders',
       'Alcohol_use_disorders', 'Exposure_to_forces_of_nature',
       'Diarrheal_diseases', 'Environmental_heat_and_cold_exposure',
       'Nutritional_deficiencies', 'Self-harm', 'Conflict_and_terrorism',
       'Diabetes_mellitus', 'Poisonings', 'Protein-energy_malnutrition',
       'Terrorism', 'Cardiovascular_diseases', 'Chronic_kidney_disease',
       'Chronic_Respiratory_diseases', 'Cirrhosis_liver_diseases',
       'Digestive_diseases', 'Acute_hepatitis', 'Alzheimer disease',
       'Parkinson disease'], aggfunc = 'sum')
Out[461]:
Acute_hepatitis Alcohol_use_disorders Alzheimer disease Cardiovascular_diseases Chronic_Respiratory_diseases Chronic_kidney_disease Cirrhosis_liver_diseases Conflict_and_terrorism Diabetes_mellitus Diarrheal_diseases Digestive_diseases Drowning Drug_use_disorders Environmental_heat_and_cold_exposure Exposure_to_forces_of_nature Fire_heat_and_hot_substances HIV/AIDS Interpersonal_violence Lower_respiratory_infections Malaria Maternal_disorders Meningitis Neonatal_disorders Neoplasms Nutritional_deficiencies Parkinson disease Poisonings Protein-energy_malnutrition Road_injuries Self-harm Terrorism Tuberculosis
Country
Afghanistan 98108.0000 3256.0000 41998.0000 1607042.0000 209857.0000 134676.0000 98419.0000 280520.0000 93207.0000 245832.0000 186959.0000 56535.0000 7094.0000 2187.0000 16770.0000 13559.0000 4282.0000 108228.0000 822179.0000 13924.0000 129621.0000 78665.0000 697534.0000 469611.0000 71453.0000 13397.0000 14531.0000 70163.0000 208332.0000 37054.0000 40240.7077 147637.0000
Africa 696194.0000 176794.0000 1639248.0000 36704118.0000 5707716.0000 3630423.0000 6347525.0000 1575617.0000 4240427.0000 26224276.0000 9900886.0000 945160.0000 75298.0000 165448.0000 34977.0000 803923.0000 29106962.0000 2178223.0000 25228868.0000 21686225.0000 2980827.0000 5559509.0000 25075847.0000 13633297.0000 5322341.0000 423800.0000 613852.0000 5145422.0000 6359218.0000 1893844.0000 10477.0771 13777914.0000
African Region 525500.0000 163833.0000 1252189.0000 24875081.0000 4564405.0000 2779058.0000 4578870.0000 1402850.0000 3602991.0000 24485553.0000 7763233.0000 801804.0000 53177.0000 152369.0000 27010.0000 659574.0000 28888674.0000 2056987.0000 23007935.0000 21462693.0000 2740727.0000 5325372.0000 22661167.0000 11094707.0000 4940860.0000 318566.0000 555599.0000 4778254.0000 4839529.0000 1651040.0000 10477.0771 13028578.0000
African Union 696194.0000 176794.0000 1639248.0000 36704118.0000 5707716.0000 3630423.0000 6347525.0000 1575617.0000 4240427.0000 26224276.0000 9900886.0000 945160.0000 75298.0000 165448.0000 34977.0000 803923.0000 29106962.0000 2178223.0000 25228868.0000 21686225.0000 2980827.0000 5559509.0000 25075847.0000 13633297.0000 5322341.0000 423800.0000 613852.0000 5145422.0000 6359218.0000 1893844.0000 10477.0771 13777914.0000
Albania 44.0000 458.0000 16551.0000 270603.0000 22632.0000 7637.0000 8717.0000 2145.0000 4054.0000 677.0000 14907.0000 2397.0000 634.0000 164.0000 89.0000 637.0000 57.0000 5242.0000 26402.0000 0.0000 247.0000 1323.0000 15568.0000 102577.0000 569.0000 4491.0000 500.0000 526.0000 8522.0000 4586.0000 4582.0668 593.0000
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
Yemen 26532.0000 1590.0000 31045.0000 1110837.0000 126525.0000 52119.0000 64136.0000 95610.0000 30812.0000 419051.0000 111536.0000 27994.0000 3718.0000 1049.0000 1131.0000 23871.0000 6276.0000 17918.0000 259045.0000 143463.0000 53611.0000 21095.0000 729558.0000 234015.0000 68939.0000 7188.0000 12561.0000 66731.0000 278327.0000 29882.0000 10522.1795 32460.0000
Yugoslavia 55034.7191 73273.9341 470807.3588 6807332.8674 1578014.9903 433745.4352 560236.0225 45163.8532 473234.7341 977530.0330 991378.9154 150391.6554 41639.5071 26844.6712 20501.0202 53338.0569 567017.1386 183790.1798 1251504.8689 381744.5348 111808.3970 154916.4135 1112262.9303 3580782.1124 199032.6876 112404.2037 38269.3333 173296.6142 534199.3708 360870.2936 113.0000 672663.2419
Zaire 27517.3596 36636.9670 235403.6794 3403666.4337 789007.4951 216872.7176 280118.0112 22581.9266 236617.3670 488765.0165 495689.4577 75195.8277 20819.7536 13422.3356 10250.5101 26669.0285 283508.5693 91895.0899 625752.4345 190872.2674 55904.1985 77458.2067 556131.4652 1790391.0562 99516.3438 56202.1019 19134.6667 86648.3071 267099.6854 180435.1468 293.0000 336331.6210
Zambia 8847.0000 2677.0000 13473.0000 360770.0000 59174.0000 41751.0000 100581.0000 159.0000 54098.0000 348764.0000 147640.0000 12809.0000 933.0000 2451.0000 73.0000 9476.0000 1175563.0000 30066.0000 345854.0000 205529.0000 28395.0000 98886.0000 300479.0000 198743.0000 95913.0000 4053.0000 9056.0000 92915.0000 56975.0000 29215.0000 8053.4258 275391.0000
Zimbabwe 3778.0000 1246.0000 20017.0000 408352.0000 71774.0000 49952.0000 55027.0000 625.0000 71176.0000 140850.0000 108691.0000 18169.0000 2271.0000 978.0000 1247.0000 14718.0000 1836042.0000 32741.0000 326145.0000 118728.0000 29802.0000 41238.0000 251875.0000 272787.0000 66723.0000 5764.0000 9113.0000 65942.0000 67207.0000 51764.0000 4567.0668 273844.0000

293 rows × 32 columns

In [344]:
afridf = new_df.pivot_table(index = 'Country', values = ['Meningitis', 'Neoplasms',
       'Fire_heat_and_hot_substances', 'Malaria', 'Drowning',
       'Interpersonal_violence', 'HIV/AIDS', 'Drug_use_disorders',
       'Tuberculosis', 'Road_injuries', 'Maternal_disorders',
       'Lower_respiratory_infections', 'Neonatal_disorders',
       'Alcohol_use_disorders', 'Exposure_to_forces_of_nature',
       'Diarrheal_diseases', 'Environmental_heat_and_cold_exposure',
       'Nutritional_deficiencies', 'Self-harm', 'Conflict_and_terrorism',
       'Diabetes_mellitus', 'Poisonings', 'Protein-energy_malnutrition',
       'Terrorism', 'Cardiovascular_diseases', 'Chronic_kidney_disease',
       'Chronic_Respiratory_diseases', 'Cirrhosis_liver_diseases',
       'Digestive_diseases', 'Acute_hepatitis', 'Alzheimer disease',
       'Parkinson disease'], aggfunc = 'sum')
In [384]:
Nigdf = afridf.loc[['Nigeria']].sum().sort_values(ascending  = False)
In [408]:
Nigdf = pd.DataFrame(Nigdf)
Nigdf.columns = ['Number of Deaths']
Nigdf
Out[408]:
Number of Deaths
Diarrheal_diseases 7449328.0000
Malaria 6422063.0000
Lower_respiratory_infections 5917528.0000
Neonatal_disorders 5262229.0000
Cardiovascular_diseases 4176488.0000
HIV/AIDS 2216718.0000
Tuberculosis 1769390.0000
Digestive_diseases 1716202.0000
Neoplasms 1618730.0000
Meningitis 1520376.0000
Cirrhosis_liver_diseases 995203.0000
Chronic_Respiratory_diseases 641714.0000
Diabetes_mellitus 541020.0000
Maternal_disorders 525566.0000
Road_injuries 487695.0000
Chronic_kidney_disease 464656.0000
Interpersonal_violence 306846.0000
Nutritional_deficiencies 286858.0000
Protein-energy_malnutrition 270470.0000
Alzheimer disease 241713.0000
Self-harm 190297.0000
Acute_hepatitis 119860.0000
Fire_heat_and_hot_substances 110784.0000
Poisonings 107604.0000
Drowning 103723.0000
Conflict_and_terrorism 78908.0000
Parkinson disease 66545.0000
Alcohol_use_disorders 28341.0000
Environmental_heat_and_cold_exposure 26363.0000
Terrorism 24070.9436
Drug_use_disorders 4897.0000
Exposure_to_forces_of_nature 1899.0000
In [420]:
fig = px.bar(Nigdf, 'Number of Deaths', color = 'Number of Deaths', title = 'Names of Death by Dieaseses')
fig.update_layout(font_family="Courier New", font_color="blue", title_font_family="Times New Roman", title_font_color="purple")
fig.show()
In [ ]:
In [430]:
new_df[['Year_of_Death','Country', 'Conflict_and_terrorism', 'Terrorism', 'Poisonings', 'HIV/AIDS']].sort_values(by=['HIV/AIDS', 'Year_of_Death'], ascending = False)[:20]
Out[430]:
Year_of_Death Country Conflict_and_terrorism Terrorism Poisonings HIV/AIDS
8000 2004 World 70562.0000 5743.0000 91491.0000 1844490.0000
8001 2005 World 59555.0000 6331.0000 92101.0000 1833561.0000
7999 2003 World 71091.0000 3317.0000 90675.0000 1809961.0000
8002 2006 World 74623.0000 9380.0000 89895.0000 1769283.0000
7998 2002 World 66446.0000 4805.0000 89958.0000 1747605.0000
8003 2007 World 71352.0000 12824.0000 88513.0000 1670624.0000
7997 2001 World 67104.0000 7729.0000 89326.0000 1663535.0000
7996 2000 World 120087.0000 4403.0000 88840.0000 1560801.0000
8004 2008 World 64747.0000 9157.0000 88731.0000 1560729.0000
44 2004 Africa 34198.0000 349.2359 20636.0000 1500899.0000
104 2004 African Union 34198.0000 349.2359 20636.0000 1500899.0000
6944 2004 Sub-Saharan Africa - World Bank region 33559.0000 349.2359 19571.0000 1498836.0000
74 2004 African Region 20148.0000 349.2359 18747.0000 1491641.0000
43 2003 Africa 39653.0000 349.2359 20517.0000 1482330.0000
103 2003 African Union 39653.0000 349.2359 20517.0000 1482330.0000
6943 2003 Sub-Saharan Africa - World Bank region 38644.0000 349.2359 19439.0000 1480387.0000
45 2005 Africa 19808.0000 349.2359 20798.0000 1479893.0000
105 2005 African Union 19808.0000 349.2359 20798.0000 1479893.0000
6945 2005 Sub-Saharan Africa - World Bank region 19353.0000 349.2359 19748.0000 1477720.0000
73 2003 African Region 30846.0000 349.2359 18612.0000 1473615.0000
In [ ]:
In [436]:
y_Nig = new_df[new_df['Country']=='Nigeria']
In [443]:
y_Nig.groupby('Year_of_Death')['Meningitis', 'Neoplasms',
       'Fire_heat_and_hot_substances', 'Malaria', 'Drowning',
       'Interpersonal_violence', 'HIV/AIDS', 'Drug_use_disorders',
       'Tuberculosis', 'Road_injuries', 'Maternal_disorders',
       'Lower_respiratory_infections', 'Neonatal_disorders',
       'Alcohol_use_disorders', 'Exposure_to_forces_of_nature',
       'Diarrheal_diseases', 'Environmental_heat_and_cold_exposure',
       'Nutritional_deficiencies', 'Self-harm', 'Conflict_and_terrorism',
       'Diabetes_mellitus', 'Poisonings', 'Protein-energy_malnutrition',
       'Terrorism', 'Cardiovascular_diseases', 'Chronic_kidney_disease',
       'Chronic_Respiratory_diseases', 'Cirrhosis_liver_diseases',
       'Digestive_diseases', 'Acute_hepatitis', 'Alzheimer disease',
       'Parkinson disease'].sum()

C:\Users\user\AppData\Local\Temp/ipykernel_8456/1325130242.py:1: FutureWarning:

Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.
Out[443]:
Meningitis Neoplasms Fire_heat_and_hot_substances Malaria Drowning Interpersonal_violence HIV/AIDS Drug_use_disorders Tuberculosis Road_injuries Maternal_disorders Lower_respiratory_infections Neonatal_disorders Alcohol_use_disorders Exposure_to_forces_of_nature Diarrheal_diseases Environmental_heat_and_cold_exposure Nutritional_deficiencies Self-harm Conflict_and_terrorism Diabetes_mellitus Poisonings Protein-energy_malnutrition Terrorism Cardiovascular_diseases Chronic_kidney_disease Chronic_Respiratory_diseases Cirrhosis_liver_diseases Digestive_diseases Acute_hepatitis Alzheimer disease Parkinson disease
Year_of_Death
1990 40226.0000 34236.0000 2876.0000 148931.0000 2887.0000 6579.0000 7723.0000 95.0000 59421.0000 11832.0000 11879.0000 169472.0000 121027.0000 743.0000 0.0000 284419.0000 728.0000 10236.0000 4330.0000 81.0000 12194.0000 2937.0000 9769.0000 349.2359 118057.0000 11660.0000 17749.0000 25713.0000 43375.0000 4292.0000 4984.0000 1431.0000
1991 41349.0000 35172.0000 2948.0000 157502.0000 2977.0000 7140.0000 11761.0000 100.0000 60481.0000 12185.0000 12398.0000 174686.0000 125598.0000 754.0000 0.0000 297403.0000 744.0000 10517.0000 4418.0000 13.0000 12561.0000 2908.0000 10039.0000 10.0000 120203.0000 11928.0000 17994.0000 26125.0000 44145.0000 4475.0000 5107.0000 1460.0000
1992 42711.0000 36234.0000 3037.0000 165722.0000 3071.0000 7951.0000 17130.0000 105.0000 62090.0000 12663.0000 12918.0000 180284.0000 130522.0000 771.0000 0.0000 281303.0000 782.0000 10624.0000 4566.0000 147.0000 13022.0000 3007.0000 10141.0000 135.0000 123006.0000 12257.0000 18313.0000 26664.0000 45145.0000 4628.0000 5255.0000 1496.0000
1993 44166.0000 37283.0000 3133.0000 173695.0000 3165.0000 8438.0000 23871.0000 110.0000 63446.0000 13082.0000 13396.0000 185946.0000 135700.0000 782.0000 0.0000 276528.0000 781.0000 10693.0000 4694.0000 3.0000 13453.0000 3107.0000 10210.0000 349.2359 125642.0000 12536.0000 18594.0000 27081.0000 46006.0000 4666.0000 5395.0000 1528.0000
1994 45460.0000 38162.0000 3205.0000 180588.0000 3256.0000 7467.0000 31881.0000 113.0000 65194.0000 13421.0000 13962.0000 192168.0000 140521.0000 788.0000 30.0000 273916.0000 800.0000 11053.0000 4803.0000 90.0000 13851.0000 3189.0000 10552.0000 15.0000 127914.0000 12756.0000 18837.0000 27378.0000 46688.0000 4825.0000 5540.0000 1557.0000
1995 46492.0000 38843.0000 3265.0000 186263.0000 3313.0000 7634.0000 40865.0000 115.0000 67625.0000 13667.0000 14061.0000 198897.0000 144810.0000 793.0000 0.0000 271658.0000 820.0000 11401.0000 4903.0000 9.0000 14172.0000 3252.0000 10874.0000 1.0000 129482.0000 12887.0000 19031.0000 27473.0000 47083.0000 5022.0000 5654.0000 1578.0000
1996 59006.0000 39405.0000 3307.0000 191437.0000 3367.0000 7900.0000 50405.0000 117.0000 68778.0000 13879.0000 14618.0000 203492.0000 148866.0000 795.0000 0.0000 274988.0000 832.0000 11568.0000 5023.0000 83.0000 14434.0000 3296.0000 11029.0000 24.0000 132523.0000 13000.0000 19112.0000 27460.0000 47296.0000 5067.0000 5754.0000 1592.0000
1997 48278.0000 40298.0000 3365.0000 197467.0000 3417.0000 8586.0000 59969.0000 119.0000 69098.0000 14199.0000 15300.0000 206728.0000 153247.0000 803.0000 0.0000 272445.0000 844.0000 11913.0000 5234.0000 452.0000 14804.0000 3362.0000 11351.0000 107.0000 135221.0000 13176.0000 19311.0000 27746.0000 47939.0000 5178.0000 5894.0000 1614.0000
1998 50009.0000 41305.0000 3435.0000 202843.0000 3478.0000 8500.0000 71604.0000 122.0000 69735.0000 14574.0000 16126.0000 209694.0000 157553.0000 812.0000 15.0000 272459.0000 857.0000 12155.0000 5472.0000 943.0000 15193.0000 3438.0000 11574.0000 9.0000 138002.0000 13336.0000 19542.0000 28018.0000 48666.0000 5246.0000 6041.0000 1637.0000
1999 50499.0000 42312.0000 3489.0000 207571.0000 3528.0000 9369.0000 82999.0000 124.0000 70661.0000 14916.0000 16891.0000 212981.0000 162025.0000 820.0000 189.0000 273760.0000 871.0000 12206.0000 5706.0000 2692.0000 15568.0000 3503.0000 11614.0000 134.0000 140664.0000 13492.0000 19824.0000 28428.0000 49645.0000 5219.0000 6186.0000 1661.0000
2000 51685.0000 43405.0000 3541.0000 212123.0000 3554.0000 10596.0000 91616.0000 124.0000 71501.0000 15229.0000 17263.0000 215544.0000 166039.0000 830.0000 36.0000 273848.0000 884.0000 11924.0000 5876.0000 3553.0000 15961.0000 3561.0000 11334.0000 0.0000 142398.0000 13777.0000 20129.0000 28957.0000 50641.0000 5179.0000 6395.0000 1719.0000
2001 52027.0000 44569.0000 3607.0000 221139.0000 3557.0000 10783.0000 99303.0000 127.0000 71286.0000 15536.0000 17960.0000 216216.0000 170248.0000 846.0000 200.0000 272987.0000 896.0000 11977.0000 6036.0000 1155.0000 16317.0000 3598.0000 11373.0000 3.0000 141888.0000 14129.0000 20422.0000 29733.0000 51745.0000 5162.0000 6595.0000 1782.0000
2002 53195.0000 46497.0000 3664.0000 226408.0000 3617.0000 9492.0000 104701.0000 132.0000 70036.0000 16012.0000 18725.0000 216774.0000 175409.0000 869.0000 0.0000 268973.0000 966.0000 11629.0000 6198.0000 2101.0000 16937.0000 3674.0000 11038.0000 28.0000 142175.0000 14684.0000 20931.0000 30913.0000 53662.0000 4836.0000 6962.0000 1890.0000
2003 53916.0000 48197.0000 3714.0000 235380.0000 3690.0000 9554.0000 108081.0000 138.0000 68360.0000 16341.0000 19295.0000 215409.0000 179486.0000 890.0000 16.0000 262567.0000 908.0000 11390.0000 6325.0000 1027.0000 17415.0000 3712.0000 10806.0000 28.0000 140179.0000 15123.0000 21239.0000 31846.0000 55084.0000 4508.0000 7262.0000 1964.0000
2004 54092.0000 50181.0000 3778.0000 236937.0000 3658.0000 11327.0000 109749.0000 144.0000 65773.0000 16690.0000 19914.0000 213336.0000 182540.0000 915.0000 94.0000 256236.0000 907.0000 10589.0000 6455.0000 1445.0000 17873.0000 3758.0000 10018.0000 41.0000 138269.0000 15493.0000 21580.0000 32887.0000 56738.0000 4210.0000 7635.0000 2064.0000
2005 54266.0000 51932.0000 3811.0000 243827.0000 3679.0000 9720.0000 109023.0000 150.0000 61754.0000 16867.0000 20274.0000 208505.0000 185687.0000 931.0000 60.0000 248877.0000 897.0000 9651.0000 6542.0000 360.0000 18145.0000 3769.0000 9105.0000 19.0000 136056.0000 15586.0000 21792.0000 33850.0000 58188.0000 3948.0000 7992.0000 2163.0000
2006 56924.0000 53931.0000 3986.0000 257505.0000 3837.0000 9940.0000 105000.0000 155.0000 58881.0000 17447.0000 20344.0000 210683.0000 189061.0000 947.0000 40.0000 254013.0000 909.0000 9620.0000 6658.0000 589.0000 18539.0000 3916.0000 9063.0000 254.0000 135125.0000 15873.0000 22119.0000 34803.0000 59910.0000 3810.0000 8385.0000 2279.0000
2007 57314.0000 55609.0000 4142.0000 267368.0000 3883.0000 10074.0000 100730.0000 161.0000 55797.0000 17720.0000 20349.0000 208401.0000 192018.0000 961.0000 91.0000 253568.0000 909.0000 9382.0000 6814.0000 628.0000 18828.0000 3954.0000 8823.0000 82.0000 133656.0000 16026.0000 22170.0000 35595.0000 61219.0000 3678.0000 8602.0000 2342.0000
2008 56276.0000 57021.0000 3986.0000 280604.0000 3745.0000 10124.0000 95773.0000 165.0000 52577.0000 17507.0000 19930.0000 202257.0000 194666.0000 966.0000 0.0000 246594.0000 892.0000 8850.0000 6883.0000 913.0000 19065.0000 3870.0000 8301.0000 72.0000 133744.0000 16136.0000 22244.0000 35955.0000 61813.0000 3501.0000 8938.0000 2447.0000
2009 59349.0000 59145.0000 4004.0000 276715.0000 3733.0000 10223.0000 89799.0000 172.0000 51184.0000 17658.0000 20001.0000 199250.0000 197097.0000 987.0000 31.0000 243669.0000 895.0000 8663.0000 7122.0000 2668.0000 19485.0000 3857.0000 8110.0000 316.0000 135318.0000 16430.0000 22529.0000 36699.0000 62992.0000 3400.0000 9213.0000 2533.0000
2010 55105.0000 61627.0000 3994.0000 266638.0000 3663.0000 11174.0000 87236.0000 182.0000 49386.0000 17801.0000 19991.0000 194723.0000 199507.0000 1017.0000 40.0000 240708.0000 893.0000 8385.0000 7346.0000 1497.0000 19988.0000 4029.0000 7832.0000 117.0000 137026.0000 16694.0000 22820.0000 37704.0000 64449.0000 3300.0000 9527.0000 2623.0000
2011 53692.0000 64551.0000 3960.0000 254072.0000 3509.0000 11819.0000 86006.0000 195.0000 47775.0000 17997.0000 20254.0000 189770.0000 201486.0000 1054.0000 185.0000 228309.0000 884.0000 7995.0000 7517.0000 1650.0000 20621.0000 3782.0000 7449.0000 447.0000 139912.0000 17019.0000 23125.0000 38456.0000 65727.0000 3205.0000 9874.0000 2719.0000
2012 52126.0000 67786.0000 4009.0000 239886.0000 3563.0000 11269.0000 82793.0000 208.0000 47582.0000 18358.0000 20112.0000 189640.0000 203428.0000 1091.0000 378.0000 221382.0000 893.0000 7943.0000 7612.0000 3091.0000 21336.0000 3808.0000 7389.0000 1508.0000 143645.0000 17483.0000 23577.0000 39274.0000 67301.0000 3128.0000 10209.0000 2828.0000
2013 52141.0000 70387.0000 4042.0000 227310.0000 3458.0000 12037.0000 77539.0000 220.0000 48787.0000 18610.0000 19685.0000 191077.0000 204573.0000 1123.0000 19.0000 215679.0000 910.0000 7542.0000 7613.0000 6260.0000 21835.0000 3809.0000 6984.0000 2014.0000 146744.0000 17827.0000 23880.0000 39606.0000 68184.0000 3040.0000 10472.0000 2917.0000
2014 52114.0000 72209.0000 4049.0000 218689.0000 3443.0000 12925.0000 74814.0000 229.0000 50413.0000 18723.0000 19240.0000 192397.0000 203887.0000 1141.0000 15.0000 210178.0000 934.0000 7149.0000 7552.0000 14156.0000 22171.0000 3789.0000 6593.0000 7781.0000 148499.0000 18290.0000 23860.0000 39342.0000 68129.0000 2935.0000 10623.0000 2945.0000
2015 48484.0000 73971.0000 4069.0000 204672.0000 3399.0000 12648.0000 75930.0000 236.0000 50844.0000 19053.0000 18644.0000 192033.0000 203212.0000 1145.0000 94.0000 203706.0000 947.0000 7025.0000 7499.0000 14635.0000 22565.0000 3779.0000 6467.0000 5559.0000 153963.0000 18731.0000 23953.0000 38674.0000 67489.0000 2807.0000 10918.0000 3025.0000
2016 49572.0000 75842.0000 4157.0000 186482.0000 3505.0000 13090.0000 77599.0000 246.0000 50424.0000 19370.0000 18441.0000 191915.0000 202317.0000 1164.0000 46.0000 200794.0000 962.0000 6884.0000 7624.0000 5064.0000 22964.0000 3834.0000 6317.0000 2165.0000 155266.0000 19144.0000 24038.0000 38874.0000 68094.0000 2761.0000 11041.0000 3053.0000
2017 48790.0000 77596.0000 4104.0000 176183.0000 3364.0000 13436.0000 79121.0000 255.0000 48699.0000 19319.0000 18156.0000 184438.0000 199123.0000 1183.0000 20.0000 198476.0000 954.0000 6490.0000 7706.0000 5077.0000 23373.0000 3767.0000 5934.0000 1805.0000 157966.0000 19428.0000 24189.0000 39654.0000 69254.0000 2699.0000 11394.0000 3130.0000
2018 46198.0000 79466.0000 4090.0000 187000.0000 3254.0000 14093.0000 81427.0000 264.0000 46524.0000 18531.0000 17789.0000 177834.0000 197179.0000 1199.0000 300.0000 188747.0000 937.0000 5908.0000 7800.0000 5440.0000 23877.0000 3699.0000 5385.0000 349.2359 160449.0000 19710.0000 24304.0000 39914.0000 69518.0000 2595.0000 11753.0000 3234.0000
2019 44914.0000 81558.0000 4017.0000 191106.0000 3153.0000 12958.0000 82270.0000 274.0000 45278.0000 18508.0000 17650.0000 172978.0000 195397.0000 1221.0000 0.0000 181138.0000 927.0000 5496.0000 7970.0000 3086.0000 24473.0000 3640.0000 4996.0000 349.2359 163496.0000 20045.0000 24506.0000 40381.0000 70077.0000 2540.0000 12113.0000 3334.0000
In [482]:
new_df[new_df['Country']== 'Nigeria'].sum()
Out[482]:
Country                                 NigeriaNigeriaNigeriaNigeriaNigeriaNigeriaNige...
Year_of_Death                                                                       60135
Meningitis                                                                   1520376.0000
Neoplasms                                                                    1618730.0000
Fire_heat_and_hot_substances                                                  110784.0000
Malaria                                                                      6422063.0000
Drowning                                                                      103723.0000
Interpersonal_violence                                                        306846.0000
HIV/AIDS                                                                     2216718.0000
Drug_use_disorders                                                              4897.0000
Tuberculosis                                                                 1769390.0000
Road_injuries                                                                 487695.0000
Maternal_disorders                                                            525566.0000
Lower_respiratory_infections                                                 5917528.0000
Neonatal_disorders                                                           5262229.0000
Alcohol_use_disorders                                                          28341.0000
Exposure_to_forces_of_nature                                                    1899.0000
Diarrheal_diseases                                                           7449328.0000
Environmental_heat_and_cold_exposure                                           26363.0000
Nutritional_deficiencies                                                      286858.0000
Self-harm                                                                     190297.0000
Conflict_and_terrorism                                                         78908.0000
Diabetes_mellitus                                                             541020.0000
Poisonings                                                                    107604.0000
Protein-energy_malnutrition                                                   270470.0000
Terrorism                                                                      24070.9436
Cardiovascular_diseases                                                      4176488.0000
Chronic_kidney_disease                                                        464656.0000
Chronic_Respiratory_diseases                                                  641714.0000
Cirrhosis_liver_diseases                                                      995203.0000
Digestive_diseases                                                           1716202.0000
Acute_hepatitis                                                               119860.0000
Alzheimer disease                                                             241713.0000
Parkinson disease                                                              66545.0000
dtype: object
In [ ]:
In [474]:
y_Nig.head().sort_values(by = 'HIV/AIDS', ascending = False)
Out[474]:
Country Year_of_Death Meningitis Neoplasms Fire_heat_and_hot_substances Malaria Drowning Interpersonal_violence HIV/AIDS Drug_use_disorders Tuberculosis Road_injuries Maternal_disorders Lower_respiratory_infections Neonatal_disorders Alcohol_use_disorders Exposure_to_forces_of_nature Diarrheal_diseases Environmental_heat_and_cold_exposure Nutritional_deficiencies Self-harm Conflict_and_terrorism Diabetes_mellitus Poisonings Protein-energy_malnutrition Terrorism Cardiovascular_diseases Chronic_kidney_disease Chronic_Respiratory_diseases Cirrhosis_liver_diseases Digestive_diseases Acute_hepatitis Alzheimer disease Parkinson disease
5135 Nigeria 2007 57314.0000 55609.0000 4142.0000 267368.0000 3883.0000 10074.0000 100730.0000 161.0000 55797.0000 17720.0000 20349.0000 208401.0000 192018.0000 961.0000 91.0000 253568.0000 909.0000 9382.0000 6814.0000 628.0000 18828.0000 3954.0000 8823.0000 82.0000 133656.0000 16026.0000 22170.0000 35595.0000 61219.0000 3678.0000 8602.0000 2342.0000
5136 Nigeria 2008 56276.0000 57021.0000 3986.0000 280604.0000 3745.0000 10124.0000 95773.0000 165.0000 52577.0000 17507.0000 19930.0000 202257.0000 194666.0000 966.0000 0.0000 246594.0000 892.0000 8850.0000 6883.0000 913.0000 19065.0000 3870.0000 8301.0000 72.0000 133744.0000 16136.0000 22244.0000 35955.0000 61813.0000 3501.0000 8938.0000 2447.0000
5137 Nigeria 2009 59349.0000 59145.0000 4004.0000 276715.0000 3733.0000 10223.0000 89799.0000 172.0000 51184.0000 17658.0000 20001.0000 199250.0000 197097.0000 987.0000 31.0000 243669.0000 895.0000 8663.0000 7122.0000 2668.0000 19485.0000 3857.0000 8110.0000 316.0000 135318.0000 16430.0000 22529.0000 36699.0000 62992.0000 3400.0000 9213.0000 2533.0000
5139 Nigeria 2016 49572.0000 75842.0000 4157.0000 186482.0000 3505.0000 13090.0000 77599.0000 246.0000 50424.0000 19370.0000 18441.0000 191915.0000 202317.0000 1164.0000 46.0000 200794.0000 962.0000 6884.0000 7624.0000 5064.0000 22964.0000 3834.0000 6317.0000 2165.0000 155266.0000 19144.0000 24038.0000 38874.0000 68094.0000 2761.0000 11041.0000 3053.0000
5138 Nigeria 2013 52141.0000 70387.0000 4042.0000 227310.0000 3458.0000 12037.0000 77539.0000 220.0000 48787.0000 18610.0000 19685.0000 191077.0000 204573.0000 1123.0000 19.0000 215679.0000 910.0000 7542.0000 7613.0000 6260.0000 21835.0000 3809.0000 6984.0000 2014.0000 146744.0000 17827.0000 23880.0000 39606.0000 68184.0000 3040.0000 10472.0000 2917.0000
In [477]:
sorted_y_Nig = y_Nig.head().sort_values(by = 'HIV/AIDS', ascending = False)
In [478]:
fig = px.bar(sorted_y_Nig, x = 'Year_of_Death', y = ['Meningitis', 'Neoplasms',
       'Fire_heat_and_hot_substances', 'Malaria', 'Drowning',
       'Interpersonal_violence', 'HIV/AIDS', 'Drug_use_disorders',
       'Tuberculosis', 'Road_injuries', 'Maternal_disorders',
       'Lower_respiratory_infections', 'Neonatal_disorders',
       'Alcohol_use_disorders', 'Exposure_to_forces_of_nature',
       'Diarrheal_diseases', 'Environmental_heat_and_cold_exposure',
       'Nutritional_deficiencies', 'Self-harm', 'Conflict_and_terrorism',
       'Diabetes_mellitus', 'Poisonings', 'Protein-energy_malnutrition',
       'Terrorism', 'Cardiovascular_diseases', 'Chronic_kidney_disease',
       'Chronic_Respiratory_diseases', 'Cirrhosis_liver_diseases',
       'Digestive_diseases', 'Acute_hepatitis', 'Alzheimer disease',
       'Parkinson disease'])
fig.show()
In [479]:
y_Nig.head().sort_values(by = 'HIV/AIDS', ascending = False)
Out[479]:
Country Year_of_Death Meningitis Neoplasms Fire_heat_and_hot_substances Malaria Drowning Interpersonal_violence HIV/AIDS Drug_use_disorders Tuberculosis Road_injuries Maternal_disorders Lower_respiratory_infections Neonatal_disorders Alcohol_use_disorders Exposure_to_forces_of_nature Diarrheal_diseases Environmental_heat_and_cold_exposure Nutritional_deficiencies Self-harm Conflict_and_terrorism Diabetes_mellitus Poisonings Protein-energy_malnutrition Terrorism Cardiovascular_diseases Chronic_kidney_disease Chronic_Respiratory_diseases Cirrhosis_liver_diseases Digestive_diseases Acute_hepatitis Alzheimer disease Parkinson disease
5135 Nigeria 2007 57314.0000 55609.0000 4142.0000 267368.0000 3883.0000 10074.0000 100730.0000 161.0000 55797.0000 17720.0000 20349.0000 208401.0000 192018.0000 961.0000 91.0000 253568.0000 909.0000 9382.0000 6814.0000 628.0000 18828.0000 3954.0000 8823.0000 82.0000 133656.0000 16026.0000 22170.0000 35595.0000 61219.0000 3678.0000 8602.0000 2342.0000
5136 Nigeria 2008 56276.0000 57021.0000 3986.0000 280604.0000 3745.0000 10124.0000 95773.0000 165.0000 52577.0000 17507.0000 19930.0000 202257.0000 194666.0000 966.0000 0.0000 246594.0000 892.0000 8850.0000 6883.0000 913.0000 19065.0000 3870.0000 8301.0000 72.0000 133744.0000 16136.0000 22244.0000 35955.0000 61813.0000 3501.0000 8938.0000 2447.0000
5137 Nigeria 2009 59349.0000 59145.0000 4004.0000 276715.0000 3733.0000 10223.0000 89799.0000 172.0000 51184.0000 17658.0000 20001.0000 199250.0000 197097.0000 987.0000 31.0000 243669.0000 895.0000 8663.0000 7122.0000 2668.0000 19485.0000 3857.0000 8110.0000 316.0000 135318.0000 16430.0000 22529.0000 36699.0000 62992.0000 3400.0000 9213.0000 2533.0000
5139 Nigeria 2016 49572.0000 75842.0000 4157.0000 186482.0000 3505.0000 13090.0000 77599.0000 246.0000 50424.0000 19370.0000 18441.0000 191915.0000 202317.0000 1164.0000 46.0000 200794.0000 962.0000 6884.0000 7624.0000 5064.0000 22964.0000 3834.0000 6317.0000 2165.0000 155266.0000 19144.0000 24038.0000 38874.0000 68094.0000 2761.0000 11041.0000 3053.0000
5138 Nigeria 2013 52141.0000 70387.0000 4042.0000 227310.0000 3458.0000 12037.0000 77539.0000 220.0000 48787.0000 18610.0000 19685.0000 191077.0000 204573.0000 1123.0000 19.0000 215679.0000 910.0000 7542.0000 7613.0000 6260.0000 21835.0000 3809.0000 6984.0000 2014.0000 146744.0000 17827.0000 23880.0000 39606.0000 68184.0000 3040.0000 10472.0000 2917.0000
In [480]:
fig = px.bar(y_Nig, x = 'Year_of_Death', y = ['Meningitis', 'Neoplasms',
       'Fire_heat_and_hot_substances', 'Malaria', 'Drowning',
       'Interpersonal_violence', 'HIV/AIDS', 'Drug_use_disorders',
       'Tuberculosis', 'Road_injuries', 'Maternal_disorders',
       'Lower_respiratory_infections', 'Neonatal_disorders',
       'Alcohol_use_disorders', 'Exposure_to_forces_of_nature',
       'Diarrheal_diseases', 'Environmental_heat_and_cold_exposure',
       'Nutritional_deficiencies', 'Self-harm', 'Conflict_and_terrorism',
       'Diabetes_mellitus', 'Poisonings', 'Protein-energy_malnutrition',
       'Terrorism', 'Cardiovascular_diseases', 'Chronic_kidney_disease',
       'Chronic_Respiratory_diseases', 'Cirrhosis_liver_diseases',
       'Digestive_diseases', 'Acute_hepatitis', 'Alzheimer disease',
       'Parkinson disease'])
fig.show()
In [ ]:
#The end. Thanks for reading!!!